extract-module-from-notebook

To extract functions from a Jupyter Notebook, you can automate refactoring frequently used helper functions into a src module. This process identifies eligible functions, generates the module, and updates the notebook with the necessary import statements.

Refactoring a large Jupyter Notebook involves moving shared helper functions into separate src modules. This modularization approach reduces clutter, increases code reuse, and makes ongoing maintenance significantly easier across complex notebooks.

When moving notebook functions to a src directory, the extraction process automatically generates the required import statements. It updates the original notebook code cells to reference the newly created src modules, ensuring the refactored notebook runs correctly.

Yes, you can set a threshold for notebook functions before refactoring. By passing a threshold parameter, such as three, the extraction targets notebooks that exceed the specified limit of helper functions and moves them to a src module.

You should modularize Python notebook functions into separate files when you have multiple reusable helper functions. Moving them to a src module prevents code duplication, supports better refactoring workflows, and improves long-term project maintainability.