Is Tinynumpy Safe?
Tinynumpy — Nerq Trust Score 59.2/100 (C grade). Score based on 2 independent trust signals. Last analyzed: 2026-11-01
Tinynumpy is a Python package with a Nerq Trust Score of 59.2/100 (C), based on 3 independent data dimensions. Last analyzed: 2026-11-01 Security: 90/100. Popularity: 60/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-11-01. Machine-readable data (JSON).
Is Tinynumpy safe?
Trust Score Breakdown — Tinynumpy has a Nerq Trust Score of 59.2/100 (C). Measured across 2 independent trust signals (as of 2026-11-01).
What is Tinynumpy's trust score?
Tinynumpy has a Nerq Trust Score of 59.2/100, 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 Tinynumpy?
Tinynumpy's strongest signal is security at 90/100. No known vulnerabilities have been detected.
What is Tinynumpy and who maintains it?
| Author | Wade Brainerd, Almar Klein |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Tinynumpy
What is Tinynumpy?
Tinynumpy is a Python package — A lightweight, pure Python, numpy compliant ndarray class.
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=tinynumpy
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Tinynumpy has a Nerq Trust Score of 59/100 (C). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Tinynumpy has a Trust Score of 59/100 (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.