Developing Data Science Notebooks

Applies engineering best practices to Jupyter notebooks so the research code is readable, reproducible, and ready to be productionized. Synthesized from: PLOS "Ten Simple Rules" (2019), Google Cloud Jupyter Notebook Manifesto, Cookiecutter Data Science (DrivenData), nbdev, papermill, and the arXiv study on notebook collaboration practices.

Core Mindset

• The notebook is exploration and narrative; the product is born from the modules extracted from it. Treat the notebook like software, not a draft.
• Code that stabilizes gets refactored into functions and modules of your own installable package. The notebook becomes the orchestrator/consumer of that package.
• A "consolidated" notebook is one you can Runtime → Restart & Run All with zero errors, top to bottom.

Checklist: Code Structure

Use this ordering when building the notebook top-down: