Jupyter

Summary

Jupyter (jupyter.org) is a framework for managing code “notebooks” of cells, mixed code, text, images, and graphics. Users access to math, science, engineering, communication, and visualization tools on an open-source, web-based platform.  Jupyter Notebook allows you to program in a web browser. Users can write live code, equations, visualizations, and narrative text, then share their Notebooks with others through email, GitHub, and the Jupyter Notebook Viewer in a single place. It is an open-source web application built for collaboration.

Features

• Language of choice: Jupyter supports over 40 programming languages, including Python, R, Julia, and Scala.
• Share notebooks: Notebooks can be shared with others using email, Dropbox, GitHub, and the Jupyter Notebook Viewer.
• Interactive output: Your code can produce rich, interactive output: HTML, images, videos, LaTeX, and custom MIME types.
• Big data integration: Leverage big data tools, such as Apache Spark, from Python, R, and Scala. Explore that same data with pandas, scikit-learn, ggplot2, TensorFlow.

Who can use it?

• Faculty
• Staff
• Students

When can I use it?

You can use this application anytime.

How much does it cost?

This service is available at no charge to the KU community. 

How do I get it?

You can install Jupyter Notebook by following the steps in the link below:

• https://jupyter.org/install.html

How can I use it?

Jupyter notebooks can be used to communicate and share materials for learning and teaching, as well as research outputs. For example:

• Share a Jupyter Notebook with instructional materials and exercises to support students learning to code. (Example link: ARC Lessons - Introduction to Programming in Python)
• Jupyter Notebook simulations to allow students to explore a problem: a coding to learn approach.
• Jupyter Notebooks is a lab book of data analysis, which includes code, results, and discussion. This could support student project work or research as a supplement to a lab report. (Example link: student project - The Bayesian Astrometric Dust Cartogropher)

Useful links

• Installation
• Starting the Notebook
• The Jupyter Notebook
• User interface components
• Notebook Examples