refactor-executor

To split a large Python script into modular libraries, you analyze the code structure to identify natural boundaries, extract related functions and classes, generate new library modules, and update the original source imports.

You should refactor a monolithic Python file into separate modules when the script exceeds maintainable size thresholds, becomes difficult to manage, and requires function extraction with dependency tracking to improve code reusability.

Dependency mapping during Python refactoring works by analyzing code structure to track relationships among extracted items, ensuring that when functions and classes move to new library modules, all import references and dependencies are correctly updated.

Yes, you can plan extraction boundaries before splitting a Python module. The refactoring process includes optional boundary planning and safety checks to identify natural split points and group related functions securely before generating libraries.

The best way to update source imports after extracting Python classes is to automate the source update process, which adjusts the original scripts to source the new libraries and automatically corrects all import paths and dependency references.

Yes, Python refactoring with function extraction handles safety checks for dependency updates. It applies safety checks during the modularization process to ensure source updates and library generation maintain code integrity and correct import paths.