A gallery of interesting IPython Notebooks · ipython/ipython Wiki · GitHub

This page is a curated collection of IPython notebooks that are notable for some reason. Feel free to add new content here, but please try to only include links to notebooks that include interesting visual or technical content; this should not simply be a dump of a Google search on every ipynb file out there.

Important contribution instructions: If you add new content, please ensure that for any notebook you link to, the link is to the rendered version using nbviewer, rather than the raw file. Simply paste the notebook URL in the nbviewer box and copy the resulting URL of the rendered version. This will make it much easier for visitors to be able to immediately access the new content.

Note that Matt Davis has conveniently written a set of bookmarklets and extensions to make it a one-click affair to load a Notebook URL into your browser of choice, directly opening into nbviewer.

Entire books or other large collections of notebooks on a topic

• First things first, how to run code in the IPython Notebook, this is one of IPython's official notebook example collection. Another useful one from this group, an explanation of our rich display system.

• An introduction to Compressed Sensing, part of Python for Signal Processing: an entire book (and blog) on the subject by Jose Unpingco.

• A beautiful matplotlib tutorial, that includes animations and 3d plots. This is part of a complete set of Lectures on scientific computing with Python. By J.R. Johansson.

• A single-atom laser model. This is one of a complete (and amazing) set of lectures on quantum mechanics and quantum optics using QuTiP by J. R. Johansson.

• An introduction to Bayesian inference, this is just chapter 1 in an ongoing book titled Probabilistic Programming and Bayesian Methods for Hackers Using Python and PyMC, by Cameron Davidson-Pilon.

• Logistic models of well switching in Bangladesh, part of the "Will it Python" blog series (repo) on Machine Learning and data analysis in Python. By Carl Vogel.

• Introduction to Python Data Structures, part of the UC Berkeley Scientific Python Bootcamp, led by Josh Bloom (repo).

• Bayesian Inference for the Mining Disaster Data Set: part of the UC Berkeley Python Computing for Science course by Josh Bloom.

Scientific computing and data analysis with the SciPy Stack

General topics in scientific computing

Pandas for data analysis

Social data

Miscellaneous topics about doing various things with the Notebook itself

Whimscal notebooks