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.
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