How to Use Exploratory Notebooks [Best Practices]

Jupyter notebooks, while polarizing in the data science community, can be invaluable for exploratory data analysis if utilized correctly, particularly from a business and product perspective. Originally stemming from IPython, notebooks became popular as researchers transitioned to industry, yet their scientific usage doesn't always align with enterprise needs, where clarity and presentation are paramount. Effective use of notebooks involves treating them as reports focused on storytelling and conclusions rather than on code, which should ideally be hidden to prioritize the results. The article emphasizes the importance of organizing notebook content with clear sections, such as an executive summary and context, and recommends moving auxiliary functions to Python modules for cleaner code. It also discusses the different methods of sharing notebooks, whether through local adaptations, third-party tools, or cloud services, each with its benefits and drawbacks. The author advises using Jupyter notebooks strictly for exploratory tasks and reporting, arguing that production elements should originate from reproducible systems like SageMaker Pipelines or Airflow DAGs.

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