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  • Table of Contents
  • Entire books or other large collections of notebooks on a topic
  • Introductory Tutorials
  • Programming and Computer Science
  • Statistics, Machine Learning, and Data Science
  • Mathematics, Physics, Chemistry, Biology
  • Earth Science and Geo-Spatial data
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  • Javascript
  • Miscellaneous topics about doing various things with the Notebook itself
  • Reproducible academic publications
  • Data-driven journalism
  • Whimsical notebooks
  • Videos of IPython being used in the wild
  • Accessing and programing a IBM quantum computer via notebooks
  • Software Architecture

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  1. Research

Awesome Jupyter Notebooks

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Last updated 2 years ago

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Related links: ๐Ÿ”— ๐Ÿ”—

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 , 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 has conveniently written a set of to make it a one-click affair to load a Notebook URL into your browser of choice, directly opening into nbviewer.

Table of Contents

Entire books or other large collections of notebooks on a topic

Introductory Tutorials

Programming and Computer Science

Statistics, Machine Learning, and Data Science

Mathematics, Physics, Chemistry, Biology

Earth Science and Geo-Spatial data

Linguistics and Text Mining

Engineering Education

Scientific computing and data analysis with the SciPy Stack

General topics in scientific computing

Social data

Psychology and Neuroscience

Machine Learning, Statistics and Probability

Physics, Chemistry and Biology

Economics and Finance

Earth science and geo-spatial data

Data visualization and plotting

Mathematics

Signal and Sound Processing

Natural Language Processing

Pandas for data analysis

General Python Programming

Notebooks in languages other than Python

These are notebooks that use [one of the IPython kernels for other languages](IPython kernels for other languages):

Julia

  • Coursework using IJulia notebooks:

  • Other collections of IJulia notebooks:

Haskell

OCaml

Ruby

Perl

F#

C#

Javascript

Miscellaneous topics about doing various things with the Notebook itself

  • Toward Data Science blogs:

Reproducible academic publications

Data-driven journalism

Whimsical notebooks

Videos of IPython being used in the wild

Of course the first thing you might try is searching for videos about IPython (1900 or so by last count on Youtube) but there are demonstrations of other applications using the power of IPython but are not mentioned is the descriptions. Below are a few such:

Accessing and programing a IBM quantum computer via notebooks

Software Architecture

First things first, how to . There is also a general from IPython. Another useful one from this collection is an explanation of our .

A , part of the fantastic by .

The code of the by C. Rossant, introducing IPython, NumPy, SciPy, Pandas and matplotlib for interactive computing and data visualization.

by

, a primer by on how to build clean software using notebooks.

, an entire course, based on forthcoming book published by Taylor and Francis; book title: "Automata and Computability: Programmer's Perspective", by Ganesh Gopalakrishnan, Professor, School of Computing, University of Utah, Salt Lake City. [in English, has Youtube videos]

, an entire introductory Python course written by . explains the educational context in an Alaskan high school where Eric is a teacher.

A series of notebooks created to help educate aspiring computer programmers and data scientists of all ages with no previous programming experience.

, a complete book on Python programming by . Note the book also exists ,

. Recitations from Tel-Aviv University introductory course to computer science, assembled as IPython notebooks by .

, a set of 15 Notebooks that cover exploratory computing, data analysis, and visualization. No prior programming knowledge required. Each Notebook includes a number of exercises (with answers) that should take less than 4 hours to complete. Developed by Mark Bakker for undergraduate engineering students at the Delft University of Technology.

, from the course by .

, a collection of algorithmic and data structure exercises covering search and sorting algorithms, stacks, queues, linked lists, graphs, backtracking and greedy problems.

, by .

(): Textbook for beginners, broken into one Jupyter Notebook per chapter. Can be using Binder.

, a notebook which explains the implementation of Linear Regression from scratch, by , author and editor at .

, this notebook explain the important library used in data science, by , author and editor at .

, a notebook which explains scraping the data from the internet from scratch, by , author and editor at .

, a notebook which explains the steps to perform Exploratory data Analysis in python from the scratch, by , author and editor at .

developed for the [Workshop On Uncertainty Quantification And Sensitivity Analysis For Cardiovascular Modeling]( by , Vinzenz Eck and Jacob T. Sturdy.

, a collection of notebooks by to accompany the book.

, a Date Science notebook which clearly explains Data Cleaning using Python with Pandas Library at a beginner level, by .

. A tutorial on network creation, analysis, information flow and influence maximization with by .

, a collection of notebooks accompanying the , by .

, as well as extra material on other tools from the scipy stack, by .

: a complete course by Verena Kaynig-Fittkau and Pavlos Protopapas from Harvard, with all lecture materials and homework sets as notebooks.

, this is just chapter 1 in an ongoing book titled , by .

: Python/PyMC3 code for a selection of models and figures from the book 'Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan', Second Edition, by John Kruschke (2015).

, an entire self-directed course by .

by , a comprehensive guide to Python for Data Science. The code of the 100 recipes is available on .

() by and .

.

, part of the UC Berkeley 2014 course taught by .

, part of a collection on by .

, part of an , by .

by .

A collections of notebooks on Hadoop, MapReduce, Spark.

The has two excellent collections of examples: and . Too many there to directly duplicate here, but they provide great learning materials on statistical modeling with Python.

. This is a complete service that includes a ready-to-run IPython instance with a collection of notebooks illustrating the use of the . Just log in and start running the examples.

, an introductory collection from the .

, one of a set on tutorials on exploratory data analysis with the by /

, part of a complete by .

, an illustration of the , that also includes and . By .

, one of the homework sets for Harvard's .

The classic by James, Witten, Hastie, Tibshirani (2013), has not one but two collections of notebooks to accompany the book with Python (instead of the book's default R examples). One by and one by .

, Python implementations of the R labs for the online course from Stanford University taught by Profs Trevor Hastie and Rob Tibshirani.

, Python implementations of the examples (originally written in R) from a famous introductory book, , by Max Kuhn and Kjell Johnson.

A collection of from multiple faculty at .

by , a tutorial presented at .

, by .

, by . A complete implementation of Adaboost in Python, with code for digit recognition.

, by , part of a .

, an in-depth tutorial on Pandas by .

. A Python tutorial for deep learning with SINGA.

, a frequently updated collection of notebooks on statistical inference, data analysis, visualization and machine learning, by .

, a tutorial for ETL (Extract, Transfer and Load) using python , loading to MySQL and working with csv files by .

, a collection of notebooks implementing the algorithms, reproducing the graphics found in the book "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani and Jerome Friedman and summary of the textbook.

, by .

, by .

A Coding Kata using Notebooks in Google Colab: Achieve .

- collection of notebooks that use convolutional neural networks (CNNs), recurrent neural networks (RNNs) and multilayer perceptrons (MLPs) to solve basic machine learning tasks like objects detection and classification, sequence-to-sequence predictions etc.

A . This is one of a complete set of by .

. This is part of , a complete collection of notebooks by .

Astrophysical simulations and analysis with : a collection of example notebooks on using various codes that yt interfaces with: , , , and . Note: the yt site currently throws an SSL warning, they seem to have an outdated or self-signed certificate.

, part of a set of tutorials on , by Greg Landrum.

. A complete set of lectures on Computational Fluid Dynamics, from 1-d linear waves to full 2-d Navier-Stokes, by .

. Lectures on applied thermodynamics using Python and the SciPy ecosystem, by .

, a complete course taught at George Washington University by .

, a collection of learning modules (each consisting of several IPython Notebooks) for a course in numerical differential equations taught at George Washington University by . Also offered as a "massive, open online course" (MOOC) on the platform.

by : Learn to interact with Python and handle data with Python; assumes no coding experience and creates a foundation in programming applied to technical contexts. With an accompanying .

by : Hands-on data analysis using a computational approach and real-life applications. With an accompanying .

by : Tour of the dynamics of change and motion using computational thinking with Python. With an accompanying .

, spectroscopy library built for integration ipython notebooks, matplotlib and pandas.

, a course by .

: Interactive lessons in bioinformatics, by .

Colour science computations with , a Python package implementing a comprehensive number of colour theory transformations and algorithms supported by a . More colour science related are available on .

The from the Book , covering several fields like Next-Generation Sequencing, Population Genetics, Phylogenetics, Genomics, Proteomics and Geo-referenced information.

is an interactive notebook that explains basic population genetics tools and techniques by building an in silico evolutionary model of RNA molecules.

. This notebook fully reproduces the research published in . The notebook uses mostly python but includes some bash and R as well and is relevant for researchers in bioinformatics and public health.

. This Python notebook uses the Jupyter-widget to visualize hierarchical clustering of gene expression and post-translational modification data from 37 lung cancer cell lines as an interactive heatmap. The notebook is part of the research project from this .

. A series of python notebooks using the package and API for materials science.

: A collection of notebooks used to determine solutions to some classical 2D elasticity problems. These were mostly live coded during class hours by as part of the course "Applied Elasticity" in the Mechanical Engineering Department of the .

, a collection of IPython notebooks with a focus on Earth Sciences, from to the .

, a tutorial series aimed at the Earth Sciences community, by .

, one of a rich , by .

, part of the blog series () on Machine Learning and data analysis in Python. By Carl Vogel.

, an executable version of a paper by Richard Styron and Eric Hetland published in Geophysical Research Letters, on earthquake probabilities.

, a blog demonstrating analyses in physical oceanography from to specialized to .

has a with material support for geospatial-data processing related blog posts. It includes notebooks about and .

is a collection of live Jupyter notebooks for seismology. It includes a fairly large number of notebooks on how to solve the acoustic and elastic wave equation with various different numerical methods. Additionally it contains notebooks with an extensive introduction to data handling and signal processing in seismology, and notebooks tackling ambient seismic noise, rotational and glacial seismology, and more.

is an introduction to programming in Python for Bachelors and Masters students in geo-fields (geology, geophysics, geography) taught by members of the . Course lessons and exercises are based on Jupyter notebooks and open for use by any interested person.

by .

, part of the collection on security-oriented data analysis with IPython & friends.

. A complete collection of notebooks accompanying by O'Reilly.

by . A collection of IPython notebooks illustrating topics in introductory chemical engineering analysis, including stoichiometry, generation-consumption analysis, mass and energy balances.

by . A collection of Jupyter notebooks in the form of lecture notes and engineering calculations for the course IMTR 1713 Sensors and Actuators taught at the .

, by

, by

, by Sandia's .

, by .

, a self-contained mini-course with exercises, by [ Joe McCarthy](

. () by Chris Fonnesbeck.

(, by Thomas Wiecki.

by .

. And another Numba example: .

, and , by .

by Justin Riley.

by Matthew Brett.

. Also available 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.

by .

by Fernando Perez.

, using .

The lmfit package provides a widget-based interface to the curve-fitting algorithms in SciPy.

by Jeff Thompson

by .

by . This notebook explains how to improve a recursive tree search with an heuristic function and to find the minimum solution to the gridmancer.

, an illustration of the , by .

A reconstruction of , by Skipper Seabold (complete ).

, which accompanies a more detailed . Here are the . By Brian Keegan.

More on .

.

. The also includes an explanatory slideshow. By Sean Taylor.

.

(in Spanish). By .

, by . is a dataset containing more than 200-million geolocated events with global coverage for 1979 to the present. Another GDELT example from David, that nicely .

, by

A geographic analysis of with GDELT, by .

, by , and .

Analyzing the , by (Vรฉlib is Paris' ).

, by .

, by (complete .)

. By .

, a data science tutorial that accompanies a from .

.

, a for the Udacity Intro to Data Science Course, by .

, by . Part of a .

, a GIS analysis of public crime data in SF, by .

is an entire course by to learn to access, munge, and analyse spatial data on social phenomena.

by includes many colorful data visualizations.

by . Intuition and simulation for the theory (Ma et al., 2006) that through probabilistic population codes, neurons can perform optimal cue combination with simple linear operations. Demonstrates that variance in cortical activity, rather than impairing sensory systems, is an adaptive mechanism to encode uncertainty in sensory measurements.

by Ariel Rokem.

. The effect of convolution of different receptive field functions and natural images is examined.

. A three-day crash course for vision researchers in programming with Python, building experiments with and , learning the fMRI multi-voxel pattern analysis with , and understading image processing in Python.

, part of the by .

, an IPython-based slide deck by .

by , a free recipe from the , a comprehensive guide to Python for Data Science.

, by .

, by .

, by .

by

by .

, by .

by

, a series based on Andrew Ng's Coursera class on machine learning. Part of a by .

, by Peter Norvig.

, by . Complimentary .

, by

, by [Jason Chin](Jason Chin).

and the by Jason K. Moore.

, by , using rdkit.

, visualizing and listening to the quantum-mechanical spectrum of Hydrogen. By .

Particle physics at the Large Hadron Collider (LHC): using : and notebooks by Alexander Mazurov and Andrey Ustyuzhanin at CERN.

, a demonstration of how IPython notebooks can be used to discuss both the theory and implementation of numerical algorithms on one page, by .

. Also available 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.

, by , . This is based on the .

series of notebooks which examines time-series data for economics and finance. Easy API to freely access data from the Federal Reserve, SEC, CFTC, stock and futures exchanges. Thus research from older notebooks can be replicated, and updated using the most current data. For example, this notebook forecasts likely Fed policy for setting the , but market sentiment across major asset classes is observable from the . Major economics indicators are renormalized: for example, various measures of , optionally with the forward-looking break-even rates derived from U.S. Treasury bonds. Other notebooks examine international markets: especially, gold and foreign exchange.

, Sequential repayment of a bond using interactive widgets and Python in Jupyter, by .

. This embeds a slideshow and a Web Spinning Globe (Cesium) in the notebook. By Massimo Di Stefano.

. Tutorial by from SciPy2013.

: common problems when plotting large datasets, and how to avoid them. By James A. Bednar.

and visualized using .

A with an interactive Hans Rosling Gapminder bubble chart from .

. Using NetCDF, Matplotlib, IPython Parallel and ffmpeg to generate video animation from time series of gridded data. By Massimo Di Stefano.

[21 Interactive, D3 Plots from matplotlib, ggplot for Python, prettyplotlib, Stack Overflow, and seaborn](

, by .

is a d3-based interactive visualization library built entirely on top of that ipywidgets infrastructure. Checkout the of Hans Rosling's .

, different from above by not depending on Plot.ly account.

(and other languages). It provides d3-like novel graphics, over large datasets, all without requiring any knowledge of Javascript. It also has a Matplotlib compatibility layer.

lets you construct visualizations very concisely in the notebook.

, by . The original .

, by .

This Python notebook shows a simple example of how to visualize a matrix file and Pandas DataFrame as an interactive heatmap (built using D3.js) using the Jupyter Widget (see ).

- a collection of 40 notebooks.

. A tutorial that styles the notebook differently to show that you can produce high-quality typography online with the Notebook. By Carl Vogel.

, combining SymPy and matplotlib. By .

(in Turkish) and By Burak Bayramli.

, an introductory companion to their . By the .

, a brief explanation and illustration of the math behind the DCT and its role in the JPEG image format, by .

, a demo of , by . PyChebfun is a pure-python implementation of the celebrated .

, an introduction to the matrix exponential, its applications, and a list of available software in Python and MATLAB. By .

, by .

, by .

, a collection of notebooks aimed at Mathematicians with no/little Python knowledge. Notebooks can be selected to serve as resources for a workshop. By .

with , Python port of of Richard Schreier's excellent , by . Several demonstrative notebooks on the package .

, by .

by

by is a notebook that achieves Seamless Image Cloning by employing the Poisson Solver in the iterative form.

by & is a notebook that achieves blind source separation, on audio signals in an attempt to approach the Cocktail Party Prblem. The problem has been tackled in two different methods - the FOBI and fastICA.

by Folgert Karsdorp & Maarten van Gompel.

by Andres Soto Villaverde.

by Andres Soto Villaverde.

by Andrรฉs Soto Villaverde

by Andrรฉs Soto Villaverde

Note that in the 'collections' section above there are also pandas-related links, such as the one for an .

, this notebook explains the basic concepts of a pandas data frame from scratch for beginners with examples, by .

, this is the notebook accompanying a by Wes McKinney, author of Pandas and the book.

, this notebook explains various operations and methods of Pandas library from the scratch with the help of an example, by .

.

.

, part of Coursera data analysis course, done in Python ().

, part of a by Taavi Burns.

, by . An enlightening discussion of how naive plotting choices subtly influence our interpretation of data.

, by .

, by , SciPy 2013. Companion videos , , ,

, this notebook explains how to become a good python programmer, by , author and editor at

, this notebook explains Python Strings from basic to advance level, by

, this notebook explains Python Tuples from basic to advance level, by

, this notebook explains Python Dictionary from basic to advance level, by

, this notebook explains Python Lists from basic to advance level with the help of an example, by .

, part of an from the .

by (part of a larger collection on ).

, an in-depth discussion of the descriptor protocol in Python, by .

, by .

, by .

, by .

, by .

Python 3 OOP series by : , , , , ,

(1) Python Dictionary, (2) Apache PySpark - GroupBy Transformation, and (3) Apache PySpark - ReduceBy Transformation by .

The IPython protocols to communicate between kernels and clients are language agnostic, and other programming language communities have started to build support for this protocol in their language. The Julia team has created , and these are some Julia notebooks:

, by .

, a detailed explanation of Julia's multiple dispatch design, by .

A on making interactive graphs with and Julia.

by

, a collection of optimization-related notebooks.

, Luis Benet and David P. Sanders

, David Sanders

, Steven G. Johnson

, Younhun Kim and Matthew Reyna

, presented at EuroSciPy '14 by Steven G. Johnson.

There exists a Haskell kernel for IPython in the .

, a solution to a cute problem involving treating English letters as generators of a large group.

, a look at how arbitrary gradient descent algorithms can be represented with a typeclass.

is an OCaml kernel for IPython

Similar to the Julia kernel there exists also a for IPython.

The interactive plotting library has some case studies using IRuby:

An example showcasing full use of the with the IPerl kernel.

Create a rich C# workbook for Android, iOS, Mac, WPF, or Console, and get instant live results as you learn these APIs.

Two IJavascript notebooks that demonstrate how to use to and

, also available in , . By Fernando Perez.

, by Jake van der Plas and available as a . Other contain many more great examples of doing interesting work with the scientific Python stack.

, also available in by Damiรกn Avila.

, this is part of a by Matthias Bussonnier.

, a gist by .

by .

by

This section contains academic papers that have been published in the peer-reviewed literature or pre-print sites such as the that include one or more notebooks that enable (even if only partially) readers to reproduce the results of the publication. If you include a publication here, please link to the journal article as well as providing the nbviewer notebook link (and any other relevant resources associated with the paper).

. . .

. That page, from the LIGO Open Science Center, contains multiple notebooks for various datasets corresponding to different events; lets you run the code right away. More details on the event as well as the original .

, by Brunk et al.

by Monica Bobra and Stathis Ilonidis (Astrophysical Journal, 2016). An , which reproduces all the results, has been permanently deposited in the .

by Alyssa Goodman et al. (Authorea Preprint, 2017). This article explains and shows with demonstrations how scholarly "papers" can morph into long-lasting rich records of scientific discourse, enriched with deep data and code linkages, interactive figures, audio, video, and commenting. It includes an interactive d3.js visualization and has an astronomical data figure with an IPYthon Notebook "behind" it.

by , 2015. Reviewed article will appear in JASTP. The reproduces the full analysis and figures exactly as they appear in the article, and is available on Github: link via .

, by and . (F1000Research 2016, 5:1574). An was used to perform the proposed RNA-Seq pipeline using public gene expression data of human cells after Zika virus infection. The computational pipeline is also version controlled and Dockerized available .

, by and . (Theoretical Population Biology, 2014). An , allowing figure reproduction, was deposited as a .

, by and (Proceedings B, 2014). An , allowing figures reproduction, was deposited as a .

, by J. Soelter et al. (Neuroimage 2014, Open Access). The allows to reproduce most figures from the paper and provides a deeper look at the data. The is also available.

. The .

. The .

. , and .

. The , the and the with the Amazon AMI information for reproducing the full paper.

. , and .

by . The and also all the data in a .

, an article in Scientific American . By from .

, by Wu, Garcรญa, Hauert and Traulsen. and .

by Christopher Bonnett (submitted to MNRAS)

by Jean-Luc R. Stevens, Judith S. Law, Jan Antolik, and James A. Bednar. Journal of Neuroscience, 33:15747-15766, 2013. [Notebook1] ( .

, by , and D. G. Cory. , , .

, by Tao Ding & Patrick D. Schloss. .

, by Sylvester, Z., Pirmez, C., Cantelli, A., & Jobe, Z. R.

, by M.L. Waskom, D. Kumaran, A.M. Gordon, J. Rissman, & A.D. Wagner. -

, Adam Hughes Also, check out the .

, Claire E. Olson, Steven B. Roberts doi: .

, and , HPTCDL'14. and

by et al. There are several to replicate results and make figures.

, Adam Hughes, Zhaowen Liu, Maryam Raftari, Mark. E Reeves. Notebooks are linked in Table 1 in the text.

, by C. Mendoza, J. Boswell, D. Ajoku, M. Bautista.

, in Scientific American (by Jake VanderPlas)

, , by and Patrick Chain.

(2016) by , , Spengler, T. and Dorling, S. R. Q.J.R. Meteorol. Soc. doi:10.1002/qj.2911. Accompanied by .

by and published in Genetics, March 2017 . All figures can be reproduced by the set of notebooks in .

by . Preprint in SocArXiv, June 2017. doi:10.17605/OSF.IO/ENRQ5. Paper is derived from a notebook converted to LaTeX with nbconvert. Notebook and materials at: , ,

, by and D. Poznanski. .

, by et al. Notebooks: , ,

Preprint in SocArXiv, December 2017. doi: 10.17605/OSF.IO/SZXDM. Notebook and materials at: , , .

, quant-ph ArXiV preprint, Nov 2016, by Fischer et al. A included in the ArXiV submission.

, gr-qc ArXiV preprint, May 2017, by Pitkin et al. with supporting notebooks and sources on GitHub.

, stat.OT ArXiV preprint by Heusser et al. A is available, that links to .

, in Journal of Neuroscience by Cole et al. A with all necessary data is available to reproduce all figures.

, in bioRxiv by Cole & Voytek. A with all necessary data is available to reproduce all figures. This repo also links to , which contains notebooks of tutorials.

, a preprint by S. Bonaretti et al. Jupyter notebooks are used as a graphical user interface for medical image processing and analysis. The paper is interactive, with links to data, software, and documentation throughout the text. Every figure caption contains links to fully reproduce graphs.

, by .

, analysis for the article by .

, by (in French).

. Here is the with discussion. By Jake van der Plas.

, part of Matt Davis' . This is a teaching tool for use with the IPython notebook that provides visual elements to understand programming concepts.

. 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.

. Using jupyter notebook, python, and numpy to solve Board Game "Penguins on Ice".

, stick figures generated with matplotlib.

, also available in . 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). by Damiรกn Avila.

. Plot your loss of weight with prognosis and motivation features.

.

, a study in data and gender in the Marvel comics universe, by and .

, an IJulia notebook by .

.

on how to learn Python featuring IPython as the platform of choice for learning!

shows IPython being used in the project

He does not show IPython in use but his IPython sticker is clear for the entire video:

on Python and data analysis features IPython as does his book

shows Plotly and IPython in use at a Montreal Python meetup.

notebook example (scroll down) illustrating how to use Qiskit and access the IBMQ quantum computers.

Letโ€™s see , by .

run code in the notebook
collection of notebooks
rich display system
great matplotlib tutorial
Lectures on Scientific Computing with Python
J.R. Johansson
IPython mini-book
Python Tutorial
Rajath Kumar M P
Growing Neat Software Architecture from Jupyter Notebooks
Guillaume Chevalier
Automata and Computability using Jupyter
Introduction to Programming (using Python)
Eric Matthes
This post
Numeric Computing is Fun
Python for Developers
Ricardo Duarte
in Portuguese
website translated into English
CS1001.py - Extended Introduction to Computer Science
Yoav Ram
Exploratory Computing with Python
Understanding evolutionary strategies and covariance matrix adaptation
Advanced Evolutionary Computation: Theory and Practice
Luis Martรญ
Code Katas in Python
Jupyter notebook activities for Part IA of the computing course (Michaelmas Term) in the Engineering Tripos at University of Cambridge
Garth Wells
Introduction to Python for Computational Science and Engineering
Hans Fangohr
executed and interacted with online
Predicting PewDiePie's daily subscribers using Linear Regression
Tanu Nanda Prabhu
Towards data science
Top P-thon Libraries Used In Data Science
Tanu Nanda Prabhu
Towards data science
Web scraping using Python with BeautifulSoup and Requests libraries
Tanu Nanda Prabhu
Towards data science
Exploratory data analysis in Python
Tanu Nanda Prabhu
Towards data science
An introductory notebook on uncertainty quantification and sensitivity analysis
https://sathercenter.berkeley.edu/peder-sather-grant/2016-grantees/)
Leif Rune Hellevik
Python Data Science Handbook Supplemental Materials
Jake VanderPlas
Data Cleaning using Python with Pandas Library
Tanu Nanda Prabhu
Social Network Analysis: From Graph Theory to Applications with Python
Networkx
Dima Goldenberg
"ISP": Introduction to Statistics with Python
book of the same name
Thomas Haslwanter
Notebooks for the exercises in Andrew Ng's online ML course, Spark and TensorFlow
John Wittenauer
AM207: Monte Carlo Methods, Stochastic Optimization
An introduction to Bayesian inference
Probabilistic Programming and Bayesian Methods for Hackers Using Python and PyMC
Cameron Davidson-Pilon
Doing Bayesian Data Analysis
Learn Data Science
Nitin Borwankar
IPython Cookbook
Cyrille Rossant
the GitHub repository
An introduction to machine learning with Python and scikit-learn
repo and overview
Hannes Schulz
Andreas Mueller
A progressive collection notebooks of the Machine Learning course by the University of Turin (with exercises)
Clustering and Regression
Introduction to Data Science
Michael Franklin
Neural Networks
machine learning
Aaron Masino
An introduction to Pandas
11-lesson tutorial on Pandas
Hernรกn Rojas
Data Science and Big Data with Python
Steve Phelps
Big Data for beginners
Statsmodels Project
in their official documentation
extra ones in their wiki
Machine Learning with the Shogun Toolbox
Shogun Toolbox
Python for Data Analysis
CU Boulder Research Computing Group
The Kaggle bulldozers competition example
copper toolkit
Daniel Rodrรญguez
Understanding model reliability
course on statistics and data analysis for psychologists
Michael Waskom
Graphical Representations of Linear Models
Seaborn statistical visualization library
Visualizing distributions of data
Representing variability in timeseries plots
Michael Waskom
Desperately Seeking Silver
CS 109 Data Science course
'An Introduction to Statistical Learning with Applications in R'
Jordi Warmenhoven
Matt Caudill
Python Notebooks for StatLearning Exercises
StatLearning: Statistical Learning
Applied Predictive Modeling with Python
Applied Predictive Modeling
four courses in foundations of data science, algorithms and databases
Columbia University's Lede Program
SciPy and OpenCV as an interactive computing environment for computer vision
Thiago Santos
SIBGRAPI 2014
Kalman and Bayesian Filters in Python
Roger Labbe
Adaboost for digit classification
Shashwat Shukla
An example machine learning notebook
Randal. S. Olson
collection in Data Analysis and Machine Learning
Pandas .head() to .tail()
Tom Augspurger
Apache SINGA tutorial
Data Science Notebooks
Donne Martin
ETL with Python
petl package
Dima Goldenberg
the-elements-of-statistical-learning
Classification on raw time series in TensorFlow with a LSTM RNN
Guillaume Chevalier
Signal forecasting with a Sequence-to-Sequence (seq2seq) Recurrent Neural Network (RNN) model in TensorFlow
Guillaume Chevalier
Clean Machine Learning From Dirty Code
Interactive Machine Learning Experiments
single-atom laser model
lectures on quantum mechanics and quantum optics using QuTiP
J.R. Johansson
2-d rigid-body transformations
Scientific Computing in Biomechanics and Motor Control
Marcos Duarte
yt
Enzo
Gadget
RAMSES
PKDGrav
Gasoline
Working with Reactions
cheminformatics and machine learning with the rdkit project
CFD Python: 12 steps to Navier-Stokes
Lorena Barba
Pytherm - Applied Thermodynamics
ATOMS
AeroPython: Aerodynamics-Hydrodynamics with Python
Lorena Barba
Practical Numerical Methods with Python
Lorena Barba
GW SEAS Open edX
Get Data Off the Ground with Python
Lorena Barba
online course
Take Off with Stats in Python
Lorena Barba
online course
Tour the dynamics of change and motion
Lorena Barba
online course
pyuvvis: tools for explorative spectroscopy
HyperPython: a practical introduction to the solution of hyperbolic conservation laws
David Ketcheson
An Introduction to Applied Bioinformatics
Greg Caporaso
colour
dedicated collection of IPython Notebooks
IPython Notebooks
colour-science.org
notebooks
Bioinformatics with Python Cookbook
Learning Population Genetics in an RNA world
An open RNA-Seq data analysis pipeline tutorial with an example of reprocessing data from a recent Zika virus study
this paper
Lung Cancer Post-Translational Modification and Gene Expression Regulation
Clustergrammer-Widget
paper
Materials Science in Python using pymatgen
pymatgen
materials project
Applied Elasticity
Jeevanjyoti Chakraborty
Indian Institute of Technology Kharagpur
EarthPy
whale tracks
flow of the Amazon
Python for Geosciences
Nikolay Koldunov
Find graffiti close to NY subway entrances
collection of notebooks on large-scale data analysis
Roy Hyunjin Han
Logistic models of well switching in Bangladesh
"Will it Python"
repo
Estimated likelihood of observing a large earthquake on a continental lowโ€angle normal fault and implications for lowโ€angle normal fault activity
python4oceanographers
resource-demanding numerical computations with functions in compiled languages
tidal analysis
visualization of various geo data using fancy things like interactive maps
Machinalis
public repo
Object Based Image Analysis
irrigation circles detection
seismo-live
Geo-Python
Department of Geosciences and Geography at University of Helsinki, Finland
Workshop on text analysis
Neal Caren
Detecting Algorithmically Generated Domains
Data Hacking
Mining the Social Web (3rd Edition)
Matthew Russell and Mikhail Klassen's book
Introduction to Chemical Engineering Analysis
Jeff Kantor
Sensors and Actuators
Andres Marrugo
Universidad Tecnolรณgica de Bolรญvar
Algorithms in IPython notebooks
Sebastian Raschka
Comparing the performance of Python compilers - Cython vs. Numba vs. Parakeet
Sebastian Raschka
A Crash Course in Python for Scientists
Rick Muller
A gentle introduction to scientific programming in Python, biased towards biologists
Mickey Atwal, Cold Spring Harbor Laboratory
Python for Data Science
https://interrelativity.com/joe).
First few lectures of the UW/Coursera course on Data Analysis
Repo
CythonGSL: a Cython interface for the GNU Scientific Library (GSL)
Project repo
Introduction to numerical computing with numpy
Steve Phelps
Using Numba to speed up numerical codes
self-organizing maps
Numpy performance tricks
blog post
Cyrille Rossant
IPython Parallel Push/Execute/Pull Demo
Understanding the design of the R "formula" objects
Comparing different approaches to evolutionary simulations
here
The Traveling Salesperson Problem
Peter Norvig
A git tutorial targeted at scientists
Running MATLAB in an IPython Notebook
pymatbridge
Interactive Curve-Fitting
A visual guide to the Python Spark API for distributed computing
A tutorial on Map-Reduce programming with Apache Spark and Python
Steve Phelps
CodeCombat gridmancer solver
Arn-O
Survival Analysis
lifelines library
Cam Davidson Pilon
Nate Silver's 538 model for the 2012 US Presidential Election
repo
Data about the Sandy Hook massacre in Newtown, Conneticut
blog post on the subject
notebook and accompanying data
gun violence analysis with Wikipedia data
An analysis of the Gaza-Israel 2012 crisis
Ranking NFL Teams
full repo
Automated processing of news media and generation of associated imagery
An analysis of national school standardized test data in Colombia using Pandas
Javier Moreno
Getting started with GDELT
David Masad
GDELT
integrates mapping visualizations
Titanic passengers, coal mining disasters, and vessel speed changes
Christopher Fonnesbeck
Indonesian conflicts in 2012
herrfz
Bioinformatic Approaches to the Computation of Poetic Meter
A. Sean Pue
C. Titus Brown
Tracy Teal
Vรฉlib dataset from Paris
Cyrille Rossant
bicycle-sharing program
Using Python to see how the Times writes about men and women
Neal Caren
Exploring graph properties of the Twitter stream with twython and NetworkX
F. Perez
gist repo with utilities here
Kaggle Competition: Titanic Machine Learning from Disaster
Andrew Conti
How clean are San Francisco's restaurants?
blog post
Zipfian Academy
NYT gender wage gap and US crime by state
Predicting usage of the subway system in NYC
final project
Asim Ihsan
An exploratory statistical analysis of the 2014 World Cup Final
Ricardo Tavares
notebook collection on football (aka soccer) analysis
San Francisco's Drug Geography
Lance Martin
Geographic Data Science
Dani Arribas-Bel
Analysis and visualization of a public OKCupid profile dataset using Python and Pandas
Alessandro Giusti
Cue Combination with Neural Populations
Will Adler
Modeling psychophysical data with non-linear functions
Visualizing mathematical models of brain cell connections
Python for Vision Research
PsychoPy
psychopy_ext
PyMVPA
Loading and visualizing fMRI data
Functional connectivity with NiLearn course
Gaรซl Varoquaux
A tutorial introduction to machine learning with sklearn
Andreas Mueller
Introduction to Machine Learning in Python with scikit-learn
Cyrille Rossant
IPython Cookbook
An introduction to Predictive Modeling in Python
Olivier Grisel
Face Recognition on a subset of the Labeled Faces in the Wild dataset
Olivier Grisel
An Introduction to Bayesian Methods for Multilevel Modeling
Chris Fonnesbeck
Introduction to Bayesian Networks
Kui Tang
Bayesian data analysis with PyMC3
Thomas Wiecki
A collection of examples for solving pattern classification problems
Sebastian Raschka
Introduction to Linear Regression using Python
Kevin Markham
Machine learning in Python
larger collection of data science notebooks
John Wittenauer
Probability, Paradox, and the Reasonable Person Principle
How Likely Would You Give A Five-Star Review on Yelp? -- Getting Your Hands Dirty with scikit-learn
Xun Tang
slides
Geodemographic Segmentation Model
Filipa Rodrigues
Writing A Genome Assembler with blasr and (I)Python
Multibody dynamics and control with Python
notebook file
Manipulation and display of chemical structures
Greg Landrum
The sound of Hydrogen
Matthias Bussonnier
ROOT in an LHCb masterclass
Notebook 1
Notebook 2
A Reaction-Diffusion Equation Solver in Python with Numpy
Georg Walther
Comparing different approaches to evolutionary simulations
here
Replication of the highly-contentious analysis of economic growth by Reinhart and Rogoff
Vincent Arel-Bundock
full repo here
widely-publicized critique of the original analysis done by Herndon, Ash, and Pollin
fecon235 for Financial Economics
Fed Funds rate
CFTC Commitment of Traders Report
inflation
Fixed Income: A Structured Bond- Interactive scenarios
Mats Gustavsson
Exploring seafloor habitats: geographic analysis using IPython Notebook with GRASS & R
Geo-Spatial Data with IPython
Kelsey Jordahl
Plotting pitfalls
US Census data
NYC Taxi data
datashader
Notebook
Plotly
Data and visualization integration via web based resources
https://nbviewer.ipython.org/gist/msund/7ac1203ded66fe8134cc).
Visualizing complex-valued functions with Matplotlib and Mayavi
Emilia Petrisor
bqplot
pythonic recreation
Wealth of Nations
A D3 Viewer for Matplotlib Visualizations
Bokeh is an interactive web visualization library for Python
HoloViews
Winner of the 2014 E. Tufte Slope Graphs contest
Pascal Schetelat
contest info on Tufte's site
matta, d3.js-based visualizations in the IPython Notebook
Eduardo Graells-Garrido
Clustergrammer Interactive Heatmap and DataFrame Viewer
Clustergrammer
paper
The Jupyter Widget Ecosystem - SciPy 2019 Tutorial on ipywidgets
Linear algebra with Cython
Exploring how smooth-looking functions can have very surprising derivatives even at low orders
Javier Moreno
A Collection of Applied Mathematics and Machine Learning Tutorials
its English Translation
Function minimization with iminuit
hard core tutorial
iminuit project
The Discrete Cosine Transform
Jim Mahoney
Chebfun in Python
PyChebfun
Olivier Verdier
Chebfun package by Battles and Trefethen
The Matrix Exponential
Sam Relton
Fractals, complex numbers, and your imagination
Caleb Fangmeier
A SymPy tutorial
Andrey Grozin
Introduction to Mathematics with Python
Vince Knight
Simulation of Delta Sigma modulators in Python
deltasigma
MATLAB Delta Sigma Toolbox
Giuseppe Venturini
README
PyOracle: Automatic analysis of musical structure
Greg Surges
A Gallery of SciPy's Window Functions for quick visual inspection and comparison
Jaidev Deshpande
Poisson Image Editing - Seamless Cloning
Dhruv Ilesh Shah
Blind Source Separation - Cocktail Party Problem
Dhruv Ilesh Shah
Shashwat Shukla
Python Programming for the Humanities
News Categorization using Multinomial Naive Bayes
Using random cross-validation for news categorization
Named Entity Recognition in French biomedical text
Named Entity Recognition in French biomedical text (Part 2)
11-lesson tutorial
Python Pandas DataFrame Basics
Tanu Nanda Prabhu
A 10-minute whirlwind tour of pandas
video presentation
Python for Data Analysis
Manipulating the data with Pandas using Python
Tanu Nanda Prabhu
Time-series analysis with Pandas
Financial data analysis with Pandas
Clustering of smartphone sensor data for human activity detection using pandas and scipy
repo
Log analysis with Pandas
group presented at PyConCa 2012
Analyzing and visualizing sun spot data with Pandas
Josh Hemann
Advanced analysis of Apache logs
Nikolay Koldunov
Statistical Data Analysis in Python
Christopher Fonnesbeck
1
2
3
4
How_to_get_started_coding_in_Python?
Tanu Nanda Prabhu
Towards data science
Python Strings from Scratch !!!
Tanu Nanda Prabhu
Python Tuples from Scratch !!!
Tanu Nanda Prabhu
Python Dictionary from Scratch !!!
Tanu Nanda Prabhu
Python Lists from Scratch !!!
Tanu Nanda Prabhu
Learning to code with Python
introduction to Python
Waterloo Python users group
Introduction to Python for Data Scientists
Steve Phelps
Data Science and Big Data
Python Descriptors Demystified
Chris Beaumont
A collection of not so obvious Python stuff you should know!
Sebastian Raschka
Key differences between Python 2.7.x and Python 3.x
Sebastian Raschka
A beginner's guide to Python's namespaces, scope resolution, and the LEGB rule
Sebastian Raschka
Sorting CSV files using the Python csv module
Sebastian Raschka
Leonardo Giordani
Part 1: Objects and types
Part 2: Classes and members
Part 3: Delegation - composition and inheritance
Part 4: Polymorphism
Part 5: Metaclasses
Part 6: Abstract Base Classes
How to Aggregate Subscriber's Interest using the 3 methods:
Abbas Taher
IJulia
Fractals 3 ways
Jeff Bezanson
The Design Impact of Multiple Dispatch
Stefan Karpinski
tutorial
Plotly
Numerical tours in Julia
Functional Geometry
Shashi Gowda
JuliaOpt notebooks
Mรฉtodos Numรฉricos Avanzados (2015-2)
Mรฉtodos Monte Carlo
Linear Partial Differential Equations: Analysis and Numerics
Julia tutorial for Computational Molecular Biology
Jiahao Chen
Christoph Ortner
Crossing Language Barriers with Julia, Scipy, and IPython
IHaskell project
IHaskell Demo Notebook
Homophone reduction
Gradient descent typeclass
iocaml
H.261 Video Decoding in OCaml
OCaml implementation of the 2048 game
Ruby kernel
IRuby Demo Notebook
SciRuby Notebooks
Nyaplot
War expenditure per GDP
Finding shape consensus among multiple geometrical polygons
display protocol
F# for Jupyter Notebooks
Xamarin Workbooks
D3
do computations and send a SVG back
play with a virtual DOM
Blogging With IPython in Blogger
blog post form
full repo here
Blogging With IPython in Octopress
blog post
notebooks by Jake
Blogging With IPython in Nikola
blog post form
Custom CSS control of the notebook
blog repo
IPython display hookery: tools to help display visual output from various sources
@deeplook
Importing IPython Notebooks as Modules
Min RK
Getting Started With Jupyter Notebooks for Teaching and Learning
Tony Hirst at OpenLearn
Bringing the best out of Jupyter Notebooks for Data Science
Boosting Your Jupyter Notebook Productivity
ArXiv
Revealing ferroelectric switching character using deep recurrent neural networks
Github page where code is located
Jupyter Paper
Raw Data
Discovery of Gravitational Waves by the LIGO collaboration
this binder
GW150914
main Physical Review Letters paper, "Observation of Gravitational Waves from a Binary Black Hole Merger"
Characterizing Strain Variation in Engineered E. coli Using a Multi-Omics-Based Workflow
Predicting Coronal Mass Ejections Using Machine Learning Methods
IPython notebook
Stanford Digital Repository
The Paper of the Future
Reply to 'Influence of cosmic ray variability on the monsoon rainfall and temperature': a false-positive in the field of solar-terrestrial research
Benjamin Laken
IPython notebook
figshare
An open RNA-Seq data analysis pipeline tutorial with an example of reprocessing data from a recent Zika virus study
Zichen Wang
Avi Ma'ayan
IPython notebook
here
The probability of improvement in Fisher's geometric model: a probabilistic approach
Yoav Ram
Lilach Hadany
IPython notebook
supplementry file
Stress-induced mutagenesis and complex adaptation
Yoav Ram
Lilach Hadany
IPython notebook
supplementry file
Automatic segmentation of odor maps in the mouse olfactory bulb using regularized non-negative matrix factorization
notebook
full code repository
Multi-tiered genomic analysis of head and neck cancer ties TP53 mutation to 3p loss, by A. Gross et al. (Nature Genetics 2014)
full collection of notebooks to replicate the results
Dog and human inflammatory bowel disease rely on overlapping yet distinct dysbiosis networks, by Vรกzquez-Baeza et al. (Nature microbiology 2016)
full collection of notebooks to replicate the results
powerlaw: a Python package for analysis of heavy-tailed distributions, by J. Alstott et al.
Notebook of examples in manuscript
ArXiv link
project repository
Collaborative cloud-enabled tools allow rapid, reproducible biological insights, by B. Ragan-Kelley et al.
main notebook
full collection of related notebooks
companion site
A Reference-Free Algorithm for Computational Normalization of Shotgun Sequencing Data, by C.T. Brown et al.
Full notebook
ArXiv link
project repository
The kinematics of the Local Group in a cosmological context
J.E. Forero-Romero et al.
Full notebook
github repo
Warming Ocean Threatens Sea Life
backed by a notebook for its main plot
Roberto de Almeida
MarinExplore
Extrapolating Weak Selection in Evolutionary Games
PLOS Comp Bio paper
Figshare link
Using neural networks to estimate redshift distributions. An application to CFHTLenS
paper
Mechanisms for stable, robust, and adaptive development of orientation maps in the primary visual cortex
https://ioam.github.io/topographica/_static/gcal_notebook.html),
Notebook2
Accelerated Randomized Benchmarking
Christopher Granade
Christopher Ferrie
New Journal of Physics 17 013042 (2015)
arXiv
GitHub repo
Dynamics and associations of microbial community types across the human body
Notebook replicating results
Variations in submarine channel sinuosity as a function of latitude and slope
Frontoparietal representations of task context support the flexible control of goal directed cognition
Github repository
Main notebook
pyparty: Intuitive Particle Processing in Python
Notebook to Generate the Published Figures
pyparty tutorial notebooks
Indication of family-specific DNA methylation patterns in developing oysters
https://dx.doi.org/10.1101/012831.
Notebook to generate results in the paper
Parallel Prefix Polymorphism Permits Parallelization, Presentation & Proof
Jiahao Chen
Alan Edelman
Website
notebook
Transcriptome Sequencing Reveals Potential Mechanism of Cryptic 3โ€™ Splice Site Selection in SF3B1-mutated Cancers
Christopher DeBoever
notebooks
A Workflow for Characterizing Nanoparticle Monolayers for Biosensors: Machine Learning on Real and Artificial SEM Images
AtomPy: An Open Atomic Data Curation Environment for Astrophysical Applications
Visualizing 4-Dimensional Asteroids
Challenges and opportunities in understanding microbial communities with metagenome assembly
accompanied by IPython Notebook tutorial
Adina Howe
Structure of a shear-line polar low
Sergeev, D. E.
Renfrew, I. A.
Notebooks to generate the published figures
Detecting High-Order Epistasis in Nonlinear Genotype-Phenotype Maps
Zachary R. Sailer
Michael J. Harms
this Github repo
Summary Analysis of the 2017 GitHub Open Source Survey
Stuart Geiger
OSF
GitHub
nbviewer
The weirdest SDSS galaxies: results from an outlier detection algorithm
D. Baron
Notebooks to replicate
Clustergrammer, a web-based heatmap visualization and analysis tool for high-dimensional biological data
Nicolas Fernandez
Fig. 3
Fig. 4
Fig. 5
Sociology: An investigation of Social Class Inequalities in General Cognitive Ability in Two British Birth Cohorts.
OSF
GitHub
nbviewer
An on-chip architecture for self-homodyned nonclassical light
supporting notebook for all calculations
A nested sampling code for targeted searches for continuous gravitational waves from pulsars
Complete repo
HyperTools: A Python toolbox for visualizing and manipulating high-dimensional data
repo with companion notebooks
the library itself, HyperTools
Nonsinusoidal beta oscillations reflect cortical pathophysiology in Parkinson's disease
repo with companion notebooks
Cycle-by-cycle analysis of neural oscillations
repo with companion notebooks
the related useful library, neurodsp
pyKNEEr: An image analysis workflow for open and reproducible research on femoral knee cartilage
The Need for Openness in Data Journalism
Brian Keegan
St. Louis County Segregation Analysis
The Ferguson Area Is Even More Segregated Than You Probably Guessed
Jeremy Singer-Vine
Size of thesis and dissertations in Quebec
Jean-Hugues Roy
XKCD-styled plots created with Matplotlib
blog post version
Van Gogh's Starry Night with ipythonblocks
ipythonblocks
Conway's Game of Life
pynguins
"People plots"
Reveal converter mini-tutorial
blog post form
Demo
Personal IPython Weight Notebook
Streaming Double Pendulum Simulation in IPython NB
Porque Charles Xavier debe cambiar a Cerebro por Python
Mai Gimรฉnez
Angela Rivera
Functional Geometry: a deconstruction of the MC Escher woodcut Square Limit
Shashi Gowda
Solving physical puzzles with a Jupyter Noteboook
Video
This video
scikit-learn
Planning and Tending the Garden: The Future of Early Childhood Python Education
Wes McKinney's speech
Python for Data Analysis
This video
Github
How to Grow Neat Software Architecture out of Jupyter Notebooks
Guillaume Chevalier
note/Awesome Jupyter Notebooks
app/Jupyter Notebook Apps
nbviewer
Matt Davis
bookmarklets and extensions
Entire books or other large collections of notebooks on a topic
Introductory Tutorials
Programming and Computer Science
Statistics, Machine Learning and Data Science
Mathematics, Physics, Chemistry, Biology
Earth Science and Geo-Spatial data
Linguistics and Text Mining
Signal Processing
Engineering Education
Scientific computing and data analysis with the SciPy Stack
General topics in scientific computing
Social data
Psychology and Neuroscience
Machine Learning, Statistics and Probability
Physics, Chemistry and Biology
Economics and Finance
Earth science and geo-spatial data
Data visualization and plotting
Mathematics
Signal, Sound and Image Processing
Natural Language Processing
Pandas for data analysis
General Python Programming
Notebooks in languages other than Python
Julia
Haskell
Ruby
Perl
F#
C#
Javascript
Miscellaneous topics about doing various things with the Notebook itself
Reproducible academic publications
Other publications using the Notebook
Data-driven journalism
Whimsical notebooks
Videos of IPython being used in the wild
Accessing an IBM quantum computer via notebooks
Software Architecture