From Notebook to Python Package in Three Refactors

From Notebook to Python Package in Three Refactors

Data projects often start in notebooks and stay there too long. Reproducibility drops, onboarding slows, and debugging becomes guesswork. Here is a transition path that keeps momentum.

Refactor 1: Extract pure functions from notebook cells

def clean_rows(rows):
    return [r for r in rows if r.get("score") is not None]

Refactor 2: Introduce a minimal package layout

project/
  src/myproj/data.py
  src/myproj/features.py
  tests/test_features.py

Refactor 3: Replace ad-hoc execution with entrypoints

def main():
    rows = load_rows("data/raw.csv")
    save_rows("data/clean.csv", clean_rows(rows))

Pitfalls

• Moving code without adding tests around extracted functions.
• Keeping hidden global state from notebook sessions.
• Packaging everything before identifying stable boundaries.

Validation

• Pipeline runs headless from CLI without notebook state.
• Unit tests protect feature logic from regression.
• Colleagues can run the project from a clean environment.