From Notebook to pip install: A Packaging Guide for Data Scientists

1. 1Create a new Python package using a tool like Cookiecutter
2. 2Organize your code into a logical structure with modules and functions
3. 3Write tests and documentation for your package
4. 4Use a version control system like Git to track changes
5. 5Publish your package on a repository like PyPI

Data scientists and engineers can benefit from this guide to make their projects more shareable and maintainable within their teams and organizations

💡 Packaging data science projects into pip installable packages makes them more transferable, reusable, and maintainable

Make your data science projects reusable and shareable by packaging them into pip installable packages!