shows a gallery of interesting notebooks in various situations. Some notebooks require specific packages. Docklet has already preinstalled many ofen-used Python3 and R packages. If the users find some packages not in Docklet, please install them manually, through pip3, R shell, etc.

  1. Entire books or other large collections of notebooks on a topic
  2. Scientific computing and data analysis with the SciPy Stack
  3. General Python Programming
  4. Notebooks in languages other than Python
  5. Miscellaneous topics about doing various things with the Notebook itself
  6. Reproducible academic publications
  7. Other publications using the Notebook
  8. Data-driven journalism
  9. Whimsical notebooks
  10. Videos of IPython being used in the wild

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


Signal and Sound Processing

Natural Language Processing

Pandas for data analysis

Note that in the 'collections' section above there are also pandas-related links, such as the one for an 11-lesson tutorial.

General Python Programming

Notebooks in languages other than Python

These are notebooks that use one of the IPython kernels for other languages:


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 IJulia, and these are some Julia notebooks:


There exists a Haskell kernel for IPython in the IHaskell project.


iocaml is an OCaml kernel for IPython


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

The interactive plotting library Nyaplot has some case studies using IRuby:


Miscellaneous topics about doing various things with the Notebook itself

Reproducible academic publications

This section contains academic papers that have been published in the peer-reviewed literature or pre-print sites such as the ArXiv 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).

  1. Reply to 'Influence of cosmic ray variability on the monsoon rainfall and temperature': a false-positive in the field of solar-terrestrial research by Benjamin Laken, 2015. Reviewed article will appear in JASTP. The IPython notebook reproduces the full analysis and figures exactly as they appear in the article, and is available on Github: link via figshare.

  2. The probability of improvement in Fisher's geometric model: a probabilistic approach, by Yoav Ram and Lilach Hadany. (Theoretical Population Biology, 2014). An IPython notebook, allowing figure reproduction, was deposited as a supplementry file.

  3. Stress-induced mutagenesis and complex adaptation, by Yoav Ram and Lilach Hadany (Proceedings B, 2014). An IPython notebook, allowing figures reproduction, was deposited as a supplementry file.

  4. Automatic segmentation of odor maps in the mouse olfactory bulb using regularized non-negative matrix factorization, by J. Soelter et al. (Neuroimage 2014, Open Access). The notebook allows to reproduce most figures from the paper and provides a deeper look at the data. The full code repository is also available.

  5. Multi-tiered genomic analysis of head and neck cancer ties TP53 mutation to 3p loss, by A. Gross et al. (Nature Genetics 2014). The full collection of notebooks to replicate the results.

  6. powerlaw: a Python package for analysis of heavy-tailed distributions, by J. Alstott et al.. Notebook of examples in manuscript, ArXiv link and project repository.

  7. Collaborative cloud-enabled tools allow rapid, reproducible biological insights, by B. Ragan-Kelley et al.. The main notebook, the full collection of related notebooks and the companion site with the Amazon AMI information for reproducing the full paper.

  8. A Reference-Free Algorithm for Computational Normalization of Shotgun Sequencing Data, by C.T. Brown et al.. Full notebook, ArXiv link and project repository.

  9. The kinematics of the Local Group in a cosmological context by J.E. Forero-Romero et al.. The Full notebook and also all the data in a github repo.

  10. Warming Ocean Threatens Sea Life, an article in Scientific American backed by a notebook for its main plot. By Roberto de Almeida from MarinExplore.

  11. Extrapolating Weak Selection in Evolutionary Games, by Wu, García, Hauert and Traulsen. PLOS Comp Bio paper and Figshare link.

  12. [Using neural networks to estimate redshift distributions. An application to CFHTLenS] ( by Christopher Bonnett paper(submitted to MNRAS)

  13. Mechanisms for stable, robust, and adaptive development of orientation maps in the primary visual cortex by Jean-Luc R. Stevens, Judith S. Law, Jan Antolik, and James A. Bednar. Journal of Neuroscience, 33:15747-15766, 2013. [Notebook1] (, Notebook2.

  14. Accelerated Randomized Benchmarking, by Christopher Granade, Christopher Ferrie and D. G. Cory. New Journal of Physics 17 013042 (2015), arXiv, GitHub repo.

  15. Dynamics and associations of microbial community types across the human body, by Tao Ding & Patrick D. Schloss. Notebook replicating results.

  16. Variations in submarine channel sinuosity as a function of latitude and slope, by Sylvester, Z., Pirmez, C., Cantelli, A., & Jobe, Z. R.

  17. Frontoparietal representations of task context support the flexible control of goal directed cognition, by M.L. Waskom, D. Kumaran, A.M. Gordon, J. Rissman, & A.D. Wagner. Github repository | Main notebook

  18. pyparty: Intuitive Particle Processing in Python, Adam Hughes Notebook to Generate the Published Figures | Also, check out the pyparty tutorial notebooks.

  19. Indication of family-specific DNA methylation patterns in developing oysters, Claire E. Olson, Steven B. Roberts doi: Notebook to generate results in the paper.

  20. Parallel Prefix Polymorphism Permits Parallelization, Presentation & Proof, Jiahao Chen and Alan Edelman, HPTCDL'14. Website and notebook

  21. Transcriptome Sequencing Reveals Potential Mechanism of Cryptic 3’ Splice Site Selection in SF3B1-mutated Cancers by Christopher DeBoever et al. There are several notebooks to replicate results and make figures.

  22. A Workflow for Characterizing Nanoparticle Monolayers for Biosensors: Machine Learning on Real and Artificial SEM Images, Adam Hughes, Zhaowen Liu, Maryam Raftari, Mark. E Reeves. Notebooks are linked in Table 1 in the text.

  23. AtomPy: An Open Atomic Data Curation Environment for Astrophysical Applications, by C. Mendoza, J. Boswell, D. Ajoku, M. Bautista.

  24. Visualizing 4-Dimensional Asteroids, in Scientific American (by Jake VanderPlas)

  25. Challenges and opportunities in understanding microbial communities with metagenome assembly, accompanied by IPython Notebook tutorial, by Adina Howe and Patrick Chain.

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:

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