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:

1. Title + preamble (Markdown, H1): what the analysis answers, its purpose, audience. Write for your future self (over-explain).
2. Imports isolated from logic — never mix import statements with computation in the same cell (breaks doc generation and hides side effects; only top-level statements count — imports inside functions/try are fine).
3. Parameters cell at the top marked with the parameters tag (dates, thresholds, paths, file names) — everything that changes between runs. Never bury config in the middle.
4. One cell = one meaningful step: one transformation, one function, one plot. Max ~100 lines per cell. Use Markdown above each cell explaining the why, not just the what.
5. Functions over duplicated code: any snippet reused between cells or notebooks becomes a function. When in doubt, wrap it.
6. Table of contents + section headers (Markdown H2/H3) for navigation; split very long notebooks into sequenced small notebooks with serialized intermediates (a top-level index notebook linking them).

Checklist: Code Quality

• No cell state dependency — restart & run all must work from clean kernel.
• %load_ext autoreload + %autoreload 2 and import your own modules when working in parallel with package code:

  %load_ext autoreload
  %autoreload 2
  from mypkg.features import make_dataset

• No duplicated logic between cells/notebooks (DRY). Signs you should refactor to a module: copying an old notebook to start a new one, copy-pasting functions between notebooks, creating classes inside cells.
• Keep code PEP8 and Clean Code: meaningful names, sensible comments, small functions.
• Options supported: ipytest for unit tests inside the notebook; a real IDE for refactoring/debugging.

Checklist: Reproducibility

• Declare the environment: pinned requirements.txt/environment.yml. Print critical dependency versions at the end (e.g. watermark extension).
• Never edit raw data (immutable): read from data/raw, write final products to data/processed.
• Version control: git + nbdime for human-readable notebook diffs, or save notebooks as plain text (.py/.md) via Jupytext — trivial diffs and code review.
• Validate reproducibility: full restart→run-all before considering it done.

Path to Production (API, Worker, DAG)

Signs the notebook is ready to be productionized (CCDS)

Step 0 — Parameterize (papermill)

1. Tag one cell as parameters (variables for dates, paths, thresholds).
2. The notebook becomes non-interactively executable:

   papermill input.ipynb output.ipynb -p start_date 2026-01-01

3. This is the gateway to running it as a scheduled job/stage.

Delivery shapes

CI/CD for notebooks (Google manifest)

• CI: every commit must validate the notebook — clean execution top-down + test suite + lint — before merge. Notebook is reviewed like a PR.
• CD: every tested version automatically generates an artifact and deploys (staging → production) via scheduler/event (e.g. Cloud Functions + Scheduler).
• Experiment logging: automatically record code, hyperparameters, data sources, results, and runtime for every execution so experiments are auditable.

Review Pass

When reviewing an existing notebook, check in order:

1. Runs clean top-to-bottom; no hidden state.
2. Params at top in a parameters cell; no config buried mid-notebook.
3. No import+compute cells; cells ≤100 lines with clear purpose.
4. No duplicated code across cells; functions used; stable logic heads toward a module.
5. Dependencies pinned; versions printed; raw data untouched.
6. Committed via nbdime/Jupytext — diffs are human-readable.
7. If it will be productionized: parameterized, CI-covered, extracted modules tested, experiments logged.

Red Flags

• Notebook that only works because only some cells were executed in a particular order.
• Hardcoded secrets, credentials, or absolute machine-specific paths inside cells.
• Long-lived notebook duplicating logic that already exists in the package.
• Claims of "done" without a clean restart→run-all verification.