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

1. 1Organize your notebook code into a modular structure using Python modules and functions.
2. 2Create a setup.py file to define your package's metadata and dependencies.
3. 3Use tools like setuptools or poetry to build and distribute your package.
4. 4Test your package by installing it locally and verifying its functionality.
5. 5Publish your package on a repository like PyPI to make it available for others to install.

Data scientists and engineers can benefit from this guide to make their projects more shareable and maintainable, and to streamline collaboration and deployment.

💡 Packaging your data science projects can make them more shareable, maintainable, and deployable, and can streamline collaboration and reuse of code.

Make your data science projects transferable and reusable by packaging them into a pip installable package!