Project Jupyter provides building blocks for interactive and exploratory computing that 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 data- and code-driven narratives (computational narratives) that combine live code, equations, narrative text, visualizations, interactive dashboards, and other media. While the Jupyter Notebook has proved to be an incredibly productive way of working interactively with code and data, it is helpful to decompose notebooks into more primitive building blocks: kernels for code execution, input areas for typing code, markdown cells for composing narrative content, output areas for showing results, terminals, etc.
Brian Granger, Sylvain Corlay, and Jason Grout offer an overview of JupyterLab, the next-generation user interface for Project Jupyter that puts Jupyter Notebooks within a powerful user interface that allows the building blocks of interactive computing to be assembled to support the wide range of interactive workflows used in data science. The fundamental idea of JupyterLab is to offer a user interface that allows users to assemble these building blocks in different ways to support interactive workflows that include, but go far beyond, Jupyter Notebooks. JupyterLab accomplishes this by providing a modular and extensible user interface that exposes these building blocks in the context of a powerful work space that allows users to arrange multiple notebooks, text editors, terminals, and output areas on a single page with multiple panels, tabs, splitters, and collapsible sidebars with a file browser, command palette, and integrated help system. The codebase and UI of JupyterLab is based on a flexible plugin system that makes it easy to extend with new components. Brian, Sylvain, and Jason demonstrate the JupyterLab interface and describe how it fits within the overall roadmap of the project.
Brian Granger is an associate professor of physics and data science at Cal Poly State University in San Luis Obispo. Brian is a leader of the IPython project, cofounder of Project Jupyter, and an active contributor to a number of other open source projects focused on data science in Python. Recently, he cocreated the Altair package for statistical visualization in Python. He is an advisory board member of NumFOCUS and a faculty fellow of the Cal Poly Center for Innovation and Entrepreneurship.
Sylvain Corlay is a quantitative researcher at QuantStack and an active contributor to the open source Project Jupyter. Sylvain also teaches finance at NYU and Columbia. He holds a PhD in quantitative finance from Université Pierre et Marie Curie.
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.
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