Jupyter Python

Notebooks as executable documents

A notebook file stores cells, metadata, and possibly outputs. A kernel owns the live Python process and mutable state. Saved output is evidence from a prior execution, not proof that the current source runs in order.

Workflow

1. Inspect notebook format version, kernelspec, language metadata, cell order, execution counts, imports, file paths, parameters, secrets, widgets, large outputs, and repository environment.
2. Choose the correct kernel from the project environment. Record interpreter and dependency versions; do not trust a familiar kernel display name.
3. Make dependencies and input paths explicit. Put reusable logic in imported modules with tests; keep the notebook for orchestration, explanation, and display.
4. Restart and execute all cells top-to-bottom in a clean process. Stop on the first unexpected error; do not use allow_errors to make a failing notebook appear successful.
5. Write the executed notebook to a separate artifact unless in-place mutation was explicitly requested. Inspect error outputs, execution counts, output size, and sensitive content.
6. Re-run from the same clean inputs and compare material artifacts. Persist scripts, data products, or reports separately when they are the canonical result.

Invariants

• Out-of-order success is hidden-state failure. A clean top-to-bottom run is the minimum reproducibility test.
• A notebook kernel can outlive cell edits and deletions; restart before proof.
• Do not embed credentials, personal row data, or huge binary/base64 output.
• Set random seeds and control clocks/network inputs when reproducibility requires them, while documenting unavoidable nondeterminism.
• Parameter cells or a small configuration object are preferable to manual edits scattered through cells.

import nbformat
from nbclient import NotebookClient

notebook = nbformat.read("analysis.ipynb", as_version=4)
client = NotebookClient(
    notebook,
    timeout=600,
    kernel_name="python3",
    resources={"metadata": {"path": "notebooks"}},
)
client.execute()
nbformat.write(notebook, "artifacts/analysis.executed.ipynb")

Read state and structure, execution and verification, and module/report boundaries.