Example Notebook

This folder contains an example reference notebook structure and approach for this project. Please try to follow this structure as closely as possible. While things will not exactly be the same for each notebook some principles we would like to try ensure are:

1. Each notebook or collection of related or dependent notebooks should live in its own folder.
2. Each notebook should have a markdown file with the same name as the notebook (or README.md if it's a single notebook folder) that explains what the notebook does and how to use it.
3. Add an "Open in Colab" badge to the top of the notebook (see the markdown cell near the top of example.ipynb as an example you can adapt).
4. Make it as easy as possible for someone to run the notebook in Google Colab or some other environment based on standard practices like providing a requirements.txt file or anything else needed to successfully run the notebook.

Running in Google Colab

At the top of the example notebook there is a code cell that will (once uncommented):

1. clone the repository into your colab instance.
2. cd into the relevant notebook directory.
3. run pip install -r requirements.txt to install the required packages.

At this point you can run the notebook as normal and the folder structure will match that of the repository and the colab notebook will be running from the same directory that the notebook lives in so relative links etc should work as expected (for example example.ipynb will read some sample data from data/data.csv).

If you are adding a notebook please try to add a similar cell to the top of the notebook so that it is easy for others to run the notebook in colab. If your notebook does not have any dependencies beyond what already comes as standard in Google Colab then you do not need such a cell, just an "Open in Colab" badge will suffice.

example.ipynb

This notebook contains an example "Open In Colab" badge and a code cell to prepare the colab environment to run the notebook. It also contains a code cell that will read in some sample data from the data folder in the repository and display it as a pandas dataframe.