[Avg. reading time: 4 minutes]
Notebooks vs IDE
| 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 |