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.
## Why?
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.
## Setting up
Jupyter Notebooks are part of the [Project Jupyter](https://jupyter.org/) suite of products. You can install it via a `pip` package, but it is more commonly installed via the [Anaconda Distribution](https://www.anaconda.com/)
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.
