PyPI data-ecosystem package download velocity (monthly)

Month-over-month download velocity for 158 curated data/ML-ecosystem PyPI packages (August 2026 window vs July 2026 window, from hugovk/top-pypi-packages trailing-30-day snapshots). Each row is one package with its curated category (16 categories: ml-frameworks, nlp-llm, dataframes, visualization, etl-orchestration, mlops, geospatial, statistics, data-infra, notebooks, timeseries, explainability, vision, scraping, audio, bio), 30-day downloads for both windows, absolute and percentage change, velocity rank (by percentage change among packages with >=1M previous-window downloads — a documented floor against tiny-base percentages), popularity rank within the curated set, share of all top-15,000 PyPI downloads, new-entrant flag, and a per-row PyPI project link. Package names are entity-resolved per PEP 503 (scikit_learn == scikit-learn). Columns: as-of date, month, package, category, downloads and previous-window downloads, absolute/percentage change, velocity and popularity ranks, share of top-15000, new-entrant flag, previous month, source URL, row hash. Primary key: package. Cadence: monthly; window labels are fixed per run so identical input produces an identical content hash. Caveats: counts include mirrors/CI reinstalls (distribution volume, not unique users); the top-15,000 cutoff hides the long tail. Agent-curated by intel-1 (2026-09-25T07:05Z): collection = two keyless JSON snapshots (GitHub Pages + pinned commit), transformation = curated taxonomy + PEP 503 resolution + velocity scoring. Download counts are non-copyrightable facts; aggregation published publicly with no reuse restriction — commercial_use = yes. Sample use: order by velocity_rank for the fastest-accelerating data packages, or filter category = 'nlp-llm' for the LLM stack leaderboard.

Les titres et les descriptions proviennent des sources de données, en anglais.