In development. Recommendations may change. If you have comments, please open an issue
This page provides practical instructions for how to work with notebooks, specifically using the VRE, but most of the advice applies anywhere. This guide is a work in progress and any contributions are welcome (contact us).
What are notebooks?¶
“Jupyter notebooks [..] allow data scientists to include their original code and interactive visuals alongside a detailed research or analysis output. This allows others to not only understand your analysis but also the story of how you got there, allowing the reader to interact directly with the data and insights.” (Justin Boylan-Toomey)
There are several ways notebooks are used:
- Exploratory messing-around (example)
- won’t be very tidy and reproducibility is low
- easy way to quickly store your ideas for later or share with somebody else
- Demonstrations and documentation (example)
- used to demonstrate how to use a particular package / system
- good way to showcase something and can be part of a wider documentation
- Tutorials (scientific or otherwise) (example)
- ordered and informative with focus on the teaching element
- can be used as a resource for a workshop
- for a deep dive, read Teaching and Learning with Jupyter
- Reproducible scientific analysis (TODO: good example?)
- high quality scientific content
- portability and reproducibility is most important
- can be supplementary material for a publication
- Basis for interactive dashboards (example)
- combining powerful libraries to deploy a low-maintenance dashboard backed by code in a notebook
Writing a notebook¶
Preamble: The top of the notebook should contain the following things to orientate the user:
- Short introduction to what the notebook contains, including links to related notebooks & relevant resources.
- List of non-standard requirements for the notebook: e.g. data accessed by the notebook; additional packages to be installed. In the context of the VRE, non-standard here refers to anything not supported by the VRE currently (we can then investigate supporting these if appropriate). For a more sophisticated setup, consider a requirements.txt and/or environment.yml to specify packages (and versions).
- Import all modules used in the notebook, and specify data file paths (use pathlib for platform-agnostic paths). This will make it clear what other resources (outside the notebook) are required to run it - if you can run this first code cell, you should be able to run the rest.
Organisation: Divide the notebook into manageable sections separated by sub-titles and descriptive text.
Refactoring: As you experiment with things, the notebook will inevitably get disorganised and hard to follow. You should occasionally review this and merge or split code cells into logical units. Before leaving the notebook (and definitely before sharing with others!), restart the kernel and run the notebook from top to bottom to ensure it is valid.
Pitfalls of notebooks¶
Out-of-order execution: It is easy to change cells, re-execute them etc., in different orders, as you iteratively explore an analysis. This can rapidly get the notebook into an ambiguous state (the code written in the notebook no longer represents what has actually been run). Avoid this with frequent review & refactoring.
Managing the namespace: A long notebook can contain too many variables to keep track of. Sometimes you may inadvertently re-use a variable name that you have used earlier, leading to unforeseen consequences. Variables should be kept available only within the scope of where they are relevant, and having too many variables defined at a given moment makes it hard for the reader to follow. Avoid this by refactoring the contents of a code cell into a function.
Re-using code across notebooks: Often you will want to re-use a recipe developed in another notebook. You can simply copy across the code from one notebook into another - this is where refactoring the code into portable documented functions will help. However, this is not a very maintainable path (do you update both occurrences of the code when you want to change it?). If the code is particularly important and often re-used, then it should be moved into an importable Python module, or even to a core package (e.g. viresclient).
- More detailed style guidance and worked examples
- Problems with notebooks: challenges with: version control, integration with IDEs, testing and CI, linting, code quality, maintainability & extensibility
- Improvements to workflow through Jupyter extensions
- Diagram showing progress of a tool from notebook (usable by this notebook) to module+notebook (usable by any notebook in this repository) to package+notebook (usable by anybody) – increasing maturity
Loading and sharing notebooks¶
Notebooks can be uploaded to JupyterLab using the “Upload” button (which means you must first download the notebooks to your computer from GitHub or elsewhere). To easily access a full repository, open a command line console and use git:
To clone a repository to your working space:
git clone https://github.com/pacesm/jupyter_notebooks.git ~/martins_notebooks
(this will clone it into
martins_notebooks within your home directory)
To clear any changes you made and fetch the latest version run:
cd ~/martins_notebooks git fetch git reset --hard origin/master
Creating a notebook repository¶
Notebooks of a certain theme should be collected together in a git repository hosted on GitHub (or equivalents). For an example, see the materials used at the IAGA Summer School 2019. This provides a central location where anyone can contribute, and it can easily be redeployed to any computing environment.
When to create a repository? If you have more than one notebook, it is better to keep them in a repository - this gives you a way to track changes and backup your work as well as making it easy to share by just pointing to a URL. You may choose to keep a repository of assorted notebooks under your GitHub account to manage and share small experiments and code snippets - these could be moved to a more documented thematic repository later. If you have a portable & reproducible analysis to share (e.g. supplementary material to a publication), this is perfect for it’s own dedicated repository. When there is more than one contributor (or you want to signal that contributions are welcome), use a repository under a GitHub organisation (e.g. Swarm-DISC, MagneticEarth, or your institution’s) - add to an existing repository if your notebooks fit the scope.
If the resource is intended to be public eventually, it is easier to make it public from the beginning (i.e. hosting it in an open repository on GitHub). This makes it easy to invite collaborators, provides a consistent workflow to save effort re-tooling later, and prevents inadvertently using non-open components that would delay the release. It also gives you access to innumerable free services available to open source projects (such as GitHub Actions). If there are issues blocking this initially (e.g. legal), you can still use a private GitHub repository with limited invited collaborators, which will be easy to make public later. Perhaps what you are working on right now is difficult to make public, but you can also consider releasing old projects - it is worth the effort to make public what you can.
Keep the README updated as the project evolves. This is the first point of call for someone coming across your repository so try to keep it brief yet informative.
- List contributors, contact info, instructions for contributing
- Provide instructions for using the notebooks (any external data or software required?)
- Describe the contents of the notebooks (consider a table of contents)
- Add badges at the top of the README - see Repository badges
Add notebooks following a naming convention:
- If the repository is a tutorial, number them in sequence:
- If there will be several similar experimentative notebooks, append/prepend author initials and dates:
- [More info]
- If the repository is a tutorial, number them in sequence:
If there are files other than notebooks, use a structure like:
. ├── LICENSE ├── README.md ├── environment.yml ├── data │ ├── ... <- Small volumes of data that cannot be robustly accessed in another way │ - TODO: For larger data, see below ├── notebooks │ ├── ... <- Jupyter notebooks └── src ├── __init__.py <- Makes src a Python module ├── ... <- Shared module for this project - This can include functions/classes used in more than one notebook - TODO: Instructions for importing from here
- License recommendations
- Handling version control
- Making portable with env/reqs specification
- Handling software and data deps (internal/external …)
- Data dependencies: - Go to the source - pull in from somewhere else (with initial build script, or within notebook) - Git-LFS - Institutional/external server (with some guarantee that it will remain accessible in the same format…) - Cloud bucket and using Intake
- Automated testing
“Badges” provide at-a-glance info and dynamic links for metadata, tools to interact with the code, information from services monitoring code health etc. For example, NBViewer renders notebooks better than GitHub. You can create a badge using code like below:
.. image:: https://img.shields.io/badge/render-nbviewer-orange.svg :target: https://nbviewer.jupyter.org/github/smithara/IAGA_SummerSchool2019/tree/master/notebooks/
Moving beyond notebooks¶
Separate guidance on creating packages: PyPI, Readthedocs, Travis-CI etc.