Should I move all cell contents to a new module?

No. You should typically only move function and class definitions. Variables such as row counts, worker settings, and constants should remain in the notebook to act as adjustable parameters.

How does this skill help with large notebooks?

It provides a structured workflow to isolate complex logic into separate .py files, making the notebook easier to read while ensuring the variables needed for data processing stay accessible in the cell.

What is the main risk when refactoring notebooks into modules?

The main risk is accidentally moving variable definitions—like configuration, file paths, or parameters—into a module, which causes downstream notebook cells to fail because those variables are no longer in the notebook's global scope.

Can this skill prevent broken imports?

Yes, it includes a verification checklist that ensures the new module is correctly imported and that the notebook state remains intact after the implementation logic is moved.

Notebook-to-Module Refactoring

Name: Notebook-to-Module Refactoring
Author: smith6jt-cop

bysmith6jt-cop

データサイエンスとML

Refactors Jupyter notebook code into reusable Python modules while preserving critical variable definitions and configuration.

概要

This skill provides a safe and verified workflow for moving code from Jupyter notebook cells into standalone Python modules. It addresses the common pitfall where extracting functions accidentally removes necessary configuration variables, breaking downstream cells. By distinguishing between implementation logic (functions) and cell-level configuration (variables), it ensures a clean codebase without compromising the notebook's execution state or data science reproducibility.

主な機能

Automated import statement generation for newly created modules
Surgical extraction of function definitions to external .py modules
Identification of configuration variables that must remain in-notebook
Pre-refactoring checklists to ensure downstream cell stability
0 GitHub stars
Failure analysis and lessons learned from complex data science environments

ユースケース

Cleaning up long notebook cells to improve readability and maintainability
Converting experimental notebook code into production-ready Python modules
Sharing common notebook utility functions across multiple project files

概要

主な機能

Automated import statement generation for newly created modules
Surgical extraction of function definitions to external .py modules
Identification of configuration variables that must remain in-notebook
Pre-refactoring checklists to ensure downstream cell stability
0 GitHub stars
Failure analysis and lessons learned from complex data science environments

ユースケース

Cleaning up long notebook cells to improve readability and maintainability
Converting experimental notebook code into production-ready Python modules
Sharing common notebook utility functions across multiple project files