Feature	Notebooks (.ipynb)	Python Scripts (.py)
Use Case - DE	Quick prototyping, visualizing intermediate steps	Production-grade ETL, orchestration scripts
Use Case - DS	EDA, model training, visualization	Packaging models, deployment scripts
Interactivity	High – ideal for step-by-step execution	Low – executed as a whole
Visualization	Built-in (matplotlib, seaborn, plotly support)	Needs explicit code to save/show plots
Version Control	Harder to diff and merge	Easy to diff/merge in Git
Reusability	Lower, unless modularized	High – can be organized into functions, modules
Execution Context	Cell-based execution	Linear, top-to-bottom
Production Readiness	Poor (unless using tools like Papermill, nbconvert)	High – standard for CI/CD & Airflow etc.
Debugging	Easy with cell-wise changes	Needs breakpoints/logging
Integration	Jupyter, Colab, Databricks Notebooks	Any IDE (VSCode, PyCharm), scheduler integration
Documentation & Teaching	Markdown + code	Docstrings and comments only
Unit Tests	Not practical	Easily written using pytest, unittest
Package Management	Ad hoc, via %pip, %conda	Managed via requirements.txt, poetry, pipenv
Using Libraries	Easy for experimentation, auto-reloads supported	Cleaner imports, better for dependency resolution