Refactor a notebook into a tested service

Notebooks are useful for exploration, but hidden state, manual cell order and local files make weak service contracts. Extract stable logic before adding an API.

Isolate a pure transformation

The engineering foundations lab calculates net values from synthetic rows.

from engineering_cases import notebook_refactor_case

result = notebook_refactor_case()
assert result["pure_function"] is True
assert result["expected"] is True
assert result["output"] == [{"id": "O1", "net": 96}]
assert result["notebook_executed"] is False

Move parsing, transformation and validation into modules with explicit inputs/outputs. Replace global variables and absolute paths with configuration. Add unit tests from representative notebook examples, then an adapter for database/API and a thin service endpoint.

Package dependencies, log correlation, handle errors and create health checks. Compare service output against the notebook on a frozen fixture before retiring the exploratory path. Keep the notebook as documented analysis if useful, but do not import its state into production.

The FDE for Freshers course bridges exploration to FastAPI, Docker and deployment.

Exercise

Take a three-cell transformation notebook, restart/run-all to expose state, extract one pure module and serve it locally with tested validation.

Continue learning

This article is part of the FDE engineering foundations sequence. Use the neighbouring tasks when you need the prerequisite or the next application.

• Review the prerequisite or neighbouring task in Explain time complexity using an integration workload.
• Continue with Review AI-generated code with a concrete checklist.

Reference: Jupyter notebook documentation.