This repository is a curated, ranked list of open-source Python machine learning libraries, known as 'Best-of Machine Learning with Python'. It functions primarily as a data curation and ecosystem analysis project. The core artifact is a `projects.yaml` file containing structured metadata for over 900 projects, including GitHub identifiers, package manager references, licenses, and category classifications. An automated GitHub Action workflow periodically fetches live metrics (stars, downloads) from GitHub and package registries, recalculates a quality score, and regenerates the `README.md` and changelog files in the `history/` directory. The repository serves as a comprehensive, living directory of the Python ML landscape, tracking trends, identifying dead or inactive projects, and providing a structured, versioned dataset for ecosystem analysis. While it produces a human-readable README, its true value lies in the structured data and the automated curation pipeline, making it a hybrid of a data repository and a content generation system.

This repository is the data source and historical archive for the 'best-of-python' curated list of open-source Python projects. It contains a central YAML configuration file (projects.yaml) that defines categories, projects, and metadata (GitHub IDs, PyPI IDs, descriptions, labels). The history/ directory holds weekly CSV snapshots of project metrics (stars, forks, downloads, issue counts, release info, computed ranks) dating from 2021 to mid-2026. The actual generation of the ranked markdown list is performed by an external tool (best-of-generator), which reads the YAML and CSV data and produces the README. The repository itself is not a runnable application or pipeline; it is a structured dataset with a consistent schema for tracking Python ecosystem project popularity and health over time. It serves as a reference for anyone wanting to analyze trends in Python open-source projects or reuse the categorization and ranking methodology for other ecosystems.