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The official Jupyter Conference
August 22-23, 2017: Training
August 23-25, 2017: Tutorials & Conference
New York, NY

The Jupyter Notebook as document: From structure to application

M Pacer (Project Jupyter | Berkeley Institute for Data Science), Jess Hamrick (UC Berkeley), Damián Avila (Anaconda Powered by Continuum Analytics)
4:10pm–4:50pm Friday, August 25, 2017
Programmatic
Location: Beekman/Sutton North Level: Intermediate
Average rating: ****.
(4.00, 1 rating)

What you'll learn

  • Learn how the structured nature of the notebook document format, combined with native tools for manipulation and creation, allows the notebook to be used across a wide range of domains and applications

Description

Jupyter notebooks are typically stored as files that contain JSON (JavaScript object notation) data that represents the user’s inputs and outputs, as well as rich metadata that represents core attributes such as the programming language of the notebook, but that is also user extensible. M Pacer, Jess Hamrick, and Damián Avila explain how the structured nature of the notebook document format, combined with native tools for manipulation and creation, allows the notebook to be used across a wide range of domains and applications. They discuss how to read existing notebooks into memory, how to programmatically create notebooks from within Python, and how the nbconvert library leverages this structure in a generic fashion to manipulate notebook contents and convert them to a variety of formats, including HTML, which makes it possible to view any web-addressable notebook via nbviewer. M, Jess, and Damián also offer an overview of other applications that demonstrate even more powerfully the capabilities of the notebook document: Nikola, a tool for building blogs using notebooks; RISE, which enables users to create executable slide shows by building off of the standard notebook interface; and nbgrader, which allows instructors to develop and grade coursework through the notebook.

Photo of M Pacer

M Pacer

Project Jupyter | Berkeley Institute for Data Science

M Pacer is a Jupyter core developer at the Berkeley Institute for Data Science (BIDS) focusing on the intersection between Jupyter and scientific publishing (with an eye toward constructing a total scientific record that is more amenable to machine learning techniques). M holds a PhD from UC Berkeley, where his research used machine learning and human experiments to study casual explanation and causal inference, and a BS from Yale University.

Photo of Jess Hamrick

Jess Hamrick

UC Berkeley

Jess Hamrick is a PhD candidate at UC Berkeley. Her research studies how people use imagination to solve problems and reason about the world and how to apply those ideas to machine learning and artificial intelligence. Jess is a member of the Jupyter Steering Council and is the lead maintainer of nbgrader, an open source tool for creating and grading assignments in the Jupyter Notebook. She holds a BS and an MEng in computer science from MIT.

Photo of Damián Avila

Damián Avila

Anaconda Powered by Continuum Analytics

Damián Avila is a senior software developer at Anaconda Powered by Continuum Analytics. A software developer, data scientist, quantitative analyst, and developer focusing on data science, finance, data visualization, and the Jupyter ecosystem, Damián has made meaningful contribution to several open source projects and has been a core developer to Jupyter, Nikola, and Bokeh. He is the creator of RISE, a “live” slideshow machinery for the Jupyter Notebook. Previously, he was a biochemist-immunologist. Damián has presented talks, tutorials, and posters at a number of national and international conferences and led tutorials on the scientific Python ecosystem. He’s a member of the Jupyter Steering Council, Python Argentina, Scientific Python Argentina, and the Quantitative Finance Club.

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