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.

• 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.

• First few lectures of the UW/Coursera course on Data Analysis. (Repo) by Chris Fonnesbeck.

• CythonGSL: a Cython interface for the GNU Scientific Library (GSL) (Project repo, by Thomas Wiecki.

• Exploring seafloor habitats: geographic analysis using IPython Notebook with GRASS & R. This embeds a slideshow and google Earth in the notebook. By Massimo Di Stefano.

• Exploring how smooth-looking functions can have very surprising derivatives even at low orders, combining SymPy and matplotlib. By Javier Moreno.

• Using Numba to speed up numerical codes. And another Numba example: self-organizing maps.

• IPython Parallel Push/Execute/Pull Demo by Justin Riley.

• Understanding the design of the R "formula" objects. By Matthew Brett.

• Multibody dynamics and control with Python and the notebook file by Jason K. Moore.

• Comparing different approaches to evolutionary simulations. Also available here to better visualization. The notebook was converted to a HTML presentation using an old nbconvert with the first developing implementation of reveal converter. By Yoav Ram.

• A Collection of Applied Mathematics and Machine Learning Tutorials (in Turkish). By Burak Bayramli.

• An introduction to machine learning with Python and scikit-learn (repo and overview) by Hannes Schulz and Andreas Mueller.

• A 10-minute whirlwind tour of pandas, this is the notebook accompanying a video presentation by Wes McKinney, author of Pandas and the Python for Data Analysis book.

• Time-series analysis with Pandas.

• Financial data analysis with Pandas.

• Data clustering, using pandas, scipy and the R magic.

• Log analysis with Pandas, part of a group presented at PyConCa 2012 by Taavi Burns.

• A reconstruction of Nate Silver's 538 model for the 2012 US Presidential Election, by Skipper Seabold (complete repo).

• Data about the Sandy Hook massacre in Newtown, Conneticut, which accompanies a more detailed blog post on the subject. Here are the notebook and accompanying data. By Brian Keegan.

• More on gun violence analysis with Wikipedia data.

• An analysis of the Gaza-Israel 2012 crisis.

• Ranking NFL Teams. The full repo also includes an explanatory slideshow. By Sean Taylor.

• Automated processing of news media and generation of associated imagery.

• An analysis of national school standardized test data in Colombia using Pandas (in Spanish). By Javier Moreno.

• Blogging With IPython in Blogger, also available in blog post form, full repo here. By Fernando Perez.

• Blogging With IPython in Octopress, by Jake van der Plas and available as a blog post. Other notebooks by Jake contain many more great examples of doing interesting work with the scientific Python stack.

• Blogging With IPython in Nikola, also available in blog post form by Damián Avila.

• Custom CSS control of the notebook, this is part of a blog repo by Matthias Bussonnier.

• XKCD-styled plots created with Matplotlib. Here is the blog post version with discussion. By Jake van der Plas.

• Van Gogh's Starry Night with ipythonblocks, part of Matt Davis' ipythonblocks. This is a teaching tool for use with the IPython notebook that provides visual elements to understand programming concepts.

• Conway's Game of Life. Interesting use of convolution operation to calculate the next state of game board, instead of obvious find neighbors and filter the board for next state.

• "People plots", stick figures generated with matplotlib.

• Reveal converter mini-tutorial, also available in blog post form. Do you want to make static html/css slideshow straight from the IPython notebook? OK, now you can do it with the reveal converter (nbconvert). Demo by Damián Avila.