Over the past couple of months, there has been an ongoing discussion about Jupyter Notebooks affectionately called the “Notebook Wars”. The genesis of the discussion is Joel Grus’ presentation I Don’t Like Notebooks and has been followed up with Tim Hopper’s response, aptly titled I Like Notebooks. There have been several follow-on posts on this topic including thoughtful analysis from Yihui Xie.
The purpose of this post is to use some of the points brought up in these discussions as a background for describing my personal best practices for the analysis I frequently perform with notebooks. In addition, this approach can be tailored for your unique situation. I think many new python users do not take the time to think through some of these items I discuss. My hope is that this article will spark some discussion and provide a framework that others can build off for making repeatable and easy to understand data analysis pipelines that fit their needs.