![]() The article concludes with an example of a notebook that implements these best practices. In this article, I will talk about best practices to implement in your notebooks covering notebook structure, coding style, abstraction, and refactoring. To get the best out of notebooks, they must have good structure and follow good document and coding conventions. At a team level, notebooks can significantly enhance knowledge sharing, traceability, and accelerating the speed at which new insights can be discovered. ![]() It's straight forward to get access to cloud compute, and the ability to mix code, outputs, and plots that notebooks offer is unparalleled. Recently there has been an influx of newcomers, and data scientists now have a wide range of implementations to choose from, such as Juptyer, Zeppelin, R Markdown, Spark Notebook, and Polynote.įor Data Scientists, spinning up notebook instances as the first step in exploratory data analysis has become second nature. This form of interactive workflow was introduced to assist data scientists in documenting their work, facilitating reproducibility, and prompting collaboration with their team members. Although considered relatively new, their history traces back to systems like Mathematica and MATLAB. Notebooks are increasingly crucial in the data scientist's toolbox.
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