Is Pyparsing Safe?
Pyparsing — Nerq Trust Score 72.0/100 (B grade). Score based on 2 independent trust signals. Last analyzed: 2026-08-11
Pyparsing is a Python package with a Nerq Trust Score of 72.0/100 (B), based on 3 independent data dimensions. Last analyzed: 2026-08-11 Security: 90/100. Popularity: 100/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-08-11. Machine-readable data (JSON).
Is Pyparsing safe?
Trust Score Breakdown — Pyparsing has a Nerq Trust Score of 72.0/100 (B). Measured across 2 independent trust signals (as of 2026-08-11).
What is Pyparsing's trust score?
Pyparsing has a Nerq Trust Score of 72.0/100, earning a B grade. This score is based on 2 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Pyparsing?
Pyparsing's strongest signal is popularity at 100/100. No known vulnerabilities have been detected.
What is Pyparsing and who maintains it?
| Author | Unknown |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Pyparsing
What is Pyparsing?
Pyparsing is a Python package — pyparsing - Classes and methods to define and execute parsing grammars.
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=pyparsing
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Pyparsing has a Nerq Trust Score of 72/100 (B). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Pyparsing has a Trust Score of 72/100 (B).
- 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.