IPython: From Interactive Computing to Computational Narratives

Sunday, 15 February 2015: 1:30 PM-4:30 PM

Room LL21F (San Jose Convention Center)

Fernando Perez, University of California, Berkleley, CA

IPython is an open source environment for interactive and parallel computing

that supports all stages in the lifecycle of a scientific idea: individual

exploration, collaborative development, large-scale production using parallel

resources, publication and education.

While it started as a tool for Scientific Computing witht the Python

programming language, the IPython project has evolved over the years into a

language-agnostic architecture that supports research, publication and

education across the spectrum of modern languages. This growth in scope was

marked in 2014 by an evolution of the project's name into "Project Jupyter", an

uumbrella effort of of open-source software tools that data science

reproducible and multi-language (Python, Julia, R, etc.). The main application

offered by Jupyter is the Jupyter Notebook, a web-based interactive computing

platform that allows users to author data- and code-driven narratives that

combine live code, equations, narrative text, interactive dashboards and other

rich media. These documents provide a complete record of a computation that can

be shared with others.

The Jupyter Notebook is being used in all areas of academic (UC Berkeley,

Stanford, UW, NYU, Cal Poly, MIT, Harvard, Columbia, etc.) and government (NASA

JPL, LBL, KBase, White House Hackathon) research as well as industry (Google,

IBM, Facebook, Oracle, Otto Group, Microsoft, Bloomberg, JP Morgan, WhatsApp,

O’Reilly, Quantopian, GraphLab, Enthought, Continuum, Authorea, NY Times, etc.)

and journalism (538, BuzzFeed, NYT, etc.). Because the architecture and

building blocks of Jupyter are open, they are being used to build numerous

other commercial and non-profit products and services. The Jupyter Notebook has

between 500,000 and 1.5 million individual users worldwide.

I will describe how a single architecture covers this entire use spectrum: the

underlying protocols provide a language-agnostic environment to explore data,

run production analyses and share results in a variety of output formats, from

websites to printed books. I will illustrate this with use cases taken from a

variety of disciplines and programming languages.