This blog post is mostly for one of my teams in which I use Jupyter Notebooks for documentation. Perhaps after reading this post, you the reader can understand why it might be beneficial to use Jupyter Notebooks as a form of documentation.
So why Jupyter Notebooks?
- Follows the literate programming approach. You can write text explaining a feature and then immediately show code and it’s result.
- It’s modifiable. If your user wants to play around with the documentation, the environment is set up for them to do so.
- It’s exportable. Let’s say another user doesn’t want to bother setting it up. Well it’s super simple to just export the notebook as a PDF and send that to them instead.
Jupyter Notebooks are part of the Project Jupyter suite of products. You can install it via a
pip package, but it is more commonly installed via the Anaconda Distribution
Once you have that installed, run
jupyter lab in the directory that you wish to execute code from. You might need to be in the
bash shell for this to work since the installer modifies those environmental variables.