Is Parquet Safe?
Parquet — Nerq Trust Score 63.8/100 (C+ grade). Score based on 2 independent trust signals. Last analyzed: 2026-03-20
Parquet is a Python package with a Nerq Trust Score of 63.8/100 (C+), based on 3 independent data dimensions. Last analyzed: 2026-03-20 Security: 90/100. Popularity: 60/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-20. Machine-readable data (JSON).
Is Parquet safe?
Trust Score Breakdown — Parquet has a Nerq Trust Score of 63.8/100 (C+). Measured across 2 independent trust signals (as of 2026-03-20).
What is Parquet's trust score?
Parquet has a Nerq Trust Score of 63.8/100, earning a C+ grade. This score is based on 2 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Parquet?
Parquet's strongest signal is security at 90/100. No known vulnerabilities have been detected.
What is Parquet and who maintains it?
| Author | Joe Crobak |
| Category | Python Packages |
| Source | N/A |
Parquet Across Platforms
Same developer/company in other registries:
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Safety Guide: Parquet
What is Parquet?
Parquet is a Python package — Python support for Parquet file format.
How to Verify Safety
Run pip audit or safety check. Review on PyPI for download stats.
You can also check the trust score via API: GET /v1/preflight?target=parquet
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Parquet has a Nerq Trust Score of 64/100 (C+). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Parquet has a Trust Score of 64/100 (C+).
- The score is a measured composite — it is not a suitability judgment. Evaluate the individual signals against your own requirements.
- Query the current measured values via the Nerq API.
Frequently Asked Questions
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Disclaimer: Nerq trust scores are automated measurements based on publicly available signals. They are not endorsements, verdicts, or guarantees of suitability. Always evaluate the signals against your own requirements.