Gold Standards for Jupyter Notebooks

Best practices for clean, concise notebooks

Photo by Alexander Grey on Unsplash

Jupyter notebooks are a popular tool for data scientists. The ability to display results and visualisations in line with the code you are writing and add rich annotation to your work makes notebooks ideal for the exploratory and research phases of data science projects.

However, the informal nature of Jupyter Notebooks can lead to bad practices. This is fine if you are one data scientist working in isolation (or maybe not if you hope you revisit your code in 6 months!), but if you are working in a team of data scientists and hope to collaborate on projects or go on holiday sometime then it is a good idea to develop a set of best practices.

Best practices for anything should not add unnecessary work to a team but they should introduce standards that easily allow team members to collaborate on projects. In my opinion, having a clear set of guidelines to follow for work is vital however, I also believe that these should be kept to the minimal set of rules you can get away with. A team is much more likely to follow a short, easy-to-remember, set of standards rather than a lengthy book of rules.

A team is much more likely to follow a short, easy-to-remember, set of standards rather than a…