An Empirical Analysis of the Python Package Index (PyPI)
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Abstract (arXiv)
In this research, we provide a comprehensive empirical summary of the Python Package Repository, PyPI, including both package metadata and source code covering 178,592 packages, 1,745,744 releases, 76,997 contributors, and 156,816,750 import statements. We provide counts and trends for packages, releases, dependencies, category classifications, licenses, and package imports, as well as authors, maintainers, and organizations. As one of the largest and oldest software repositories as of publication, PyPI provides insight not just into the Python ecosystem today, but also trends in software development and licensing more broadly over time. Within PyPI, we find that the growth of the repository has been robust under all measures, with a compound annual growth rate of 47% for active packages, 39% for new authors, and 61% for new import statements over the last 15 years. As with many similar social systems, we find a number of highly right-skewed distributions, including the distribution of releases per package, packages and releases per author, imports per package, and size per package and release. However, we also find that most packages are contributed by single individuals, not multiple individuals or organizations. The data, methods, and calculations herein provide an anchor for public discourse on PyPI and serve as a foundation for future research on the Python software ecosystem.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jul 2019.
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Citing papers per year: 2024: 1; 2025: 1; 2026: 3.
5 papers.
- 2024What do we know about Hugging Face? A systematic literature review and quantitative validation of qualitative claimscited by 8
- 2026Latent Reuse in Agent Skills: Multi-modal Clone Detection at Ecosystem Scalecited by 4
- 2025Empirical Evaluation of AI-Assisted Software Package Selection: A Knowledge Graph Approachcited by 2
- 2026SetupX: Can LLM Agents Learn from Past Failures in Functionality-Correct Code Repository Setup?cited by 1
- 2026From Adoption to Deployment: A Qualitative Study on AI Integration in Software Development Practicecited by 0
- 2026From Adoption to Deployment: A Qualitative Study on AI Integration in Software Development Practice18 Jul 2026
- 2026SetupX: Can LLM Agents Learn from Past Failures in Functionality-Correct Code Repository Setup?25 May 2026
- 2026Latent Reuse in Agent Skills: Multi-modal Clone Detection at Ecosystem Scale23 Mar 2026
- 2025Empirical Evaluation of AI-Assisted Software Package Selection: A Knowledge Graph Approach6 Aug 2025
- 2024What do we know about Hugging Face? A systematic literature review and quantitative validation of qualitative claims12 Jun 2024