Jupyter Notebook Best Practices. Every third Thursday of the month, we feature a classic post from the earlier days of our company, gently updated as appropriate. Because Jupyter Notebooks are a relatively recently-developed tool, they don’t (yet) follow or encourage consensus-based software development best practices. When it comes to data science solutions, there’s always a need for fast prototyping. Data scientists, typically collaborating on a small project that involves experimentation, often feel they don’t need to adhere to any engineering best practices. Concise advice to use Jupyter notebooks more effectively. Using a Jupyter notebook template (which sets up default imports and structure) and the Table of Contents (toc2) extension, which automatically numbers headings. Follow. Editor’s note: Welcome to Throwback Thursdays! Dominik Haitz. Be it a sophisticated face recognition algorithm or a simple regression model, having a model that allows you to easily test and validate ideas is incredibly valuable. Best Practices for Jupyter Notebooks. Jupyter Notebook Best Practices for Data Science September 15th, 2016.