Untitled12.ipynb
summary
Live demo shows messy notebooks turning into a reproducible pipeline by turning cleaning, feature engineering and aggregation into small functions and chaining them with pandas pipe. It uses JupyterLab, pandas, decorators for timing and a copying start_pipeline step to avoid side effects, and surfaces a log decorator for per-step metrics. A real package scikit-lego and examples exist with about 2,000 downloads per month and the pattern yields testable, maintainable code.
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chapters (7)
- 0:00Introduction
- 1:05Agenda
- 1:35Introducing a workflow
- 4:15Live demo with bad practices
- 13:00Demo but with better practices
- 26:30Scikit-lego
- 30:57Closing thoughts
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