Notebook Refactor

Jupyter notebooks are great for research but risky for production. This skill helps you safely migrate code out of cells and into testable modules.

The Refactor Checklist

1. Identify Logic: Find the cells that actually perform data transformations (ignore the plotting cells for now).
2. Extract Functions: Move cell code into well-named functions with type hints.
3. Handle Config: Extract hard-coded paths and variables into a config.yaml or .env file.
4. Parameterize: Ensure your main script can take arguments (e.g., input path).
5. Unit Test: Write a small test for your new function using representative dummy data.

Before vs. After

• Before: A notebook with 50 cells, global variables, and print() statements everywhere.
• After: A single pipeline.py that imports functions from utils.py and logs progress.

Best Practices

• Use a .ipynb to .py exporter for a head start, but always manually clean the result.
• Keep your plotting logic separate from your transformation logic.