Presented By O'Reilly and Cloudera
Make Data Work
September 25–26, 2017: Training
September 26–28, 2017: Tutorials & Conference
New York, NY

JupyterLab: Building blocks for interactive computing

Moderated by:
Jason Grout (Bloomberg LP)
Jessica Forde (Jupyter)
2:05pm2:45pm Wednesday, September 27, 2017
Average rating: ****.
(4.80, 5 ratings)

Who is this presentation for?

  • Data scientists and Jupyter users

Prerequisite knowledge

  • Familiarity with interactive computing environments for data science, such as the Jupyter Notebook

What you'll learn

  • Explore JupyterLab, the next generation of the Jupyter Notebook


Project Jupyter provides building blocks for interactive and exploratory computing, which make science and data science reproducible across over 40 programming languages (Python, Julia, R, etc.). Central to the project is the Jupyter Notebook, a web-based interactive computing platform that allows users to author “computational narratives” that combine live code, equations, narrative text, visualizations, interactive dashboards, and other media. Jason Grout and Jessica Forde offer an overview of JupyterLab, the next generation of the Jupyter Notebook, demonstrate how to use third-party plugins to extend and customize many aspects of JupyterLab, and explain how it fits within the overall vision of Project Jupyter.

JupyterLab goes beyond the classic Jupyter Notebook by providing a flexible and extensible web application with a set of reusable components. Users can arrange multiple notebooks, text editors, terminals, output areas, and custom components using tabs and collapsible sidebars. These components are carefully designed to enable the user to use them together (for example, a user can send code from a file to a console with a keystroke) or separately (for example, a user can drag an interactive output from a notebook into a new tab to work with it alone) to support novel data-driven workflows.

JupyterLab is based on a flexible application plugin system provided by PhosphorJS that makes it easy to customize existing components or extend it with new components. For example, users can install or write third-party plugins to view custom file formats, such as GeoJSON, interact with external services, such as Dask or Apache Spark, or display their data in effective and useful ways, such as interactive maps, tables, or plots.

Photo of Jason Grout

Jason Grout

Bloomberg LP

Jason Grout is a Jupyter developer at Bloomberg, working primarily on JupyterLab and the interactive Jupyter widgets library. He has also been a major contributor to the open source Sage mathematical software system and co-organizes the PyDataNYC Meetup. Previously, Jason was an assistant professor of mathematics at Drake University in Des Moines, Iowa. He holds a PhD in mathematics from Brigham Young University.

Photo of Jessica Forde

Jessica Forde


Jessica Forde is a technical writer for Project Jupyter. Her previous open source projects include datamicroscopes, a Bayesian nonparametrics library in Python, and density, a tool for Columbia University study spaces based on wireless device data.