Is Numpy Safe?
Numpy — Nerq Trust Score 90.0/100 (A+ grade). Score based on 2 independent trust signals. Last analyzed: 2026-09-08
Numpy is a Python package with a Nerq Trust Score of 90.0/100 (A+), based on 3 independent data dimensions. Last analyzed: 2026-09-08 Security: 90/100. Popularity: 100/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-09-08. Machine-readable data (JSON).
Is Numpy safe?
Trust Score Breakdown — Numpy has a Nerq Trust Score of 90.0/100 (A+). Measured across 2 independent trust signals (as of 2026-09-08).
What is Numpy's trust score?
Numpy has a Nerq Trust Score of 90.0/100, earning a A+ grade. This score is based on 2 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Numpy?
Numpy's strongest signal is popularity at 100/100. No known vulnerabilities have been detected.
What is Numpy and who maintains it?
| Author | Travis E. Oliphant et al. |
| Category | Python Packages |
| Source | N/A |
Numpy Across Platforms
Same developer/company in other registries:
Similar Pypi by Trust Score
Safety Guide: Numpy
What is Numpy?
Numpy is a Python package — Fundamental package for array computing in Python.
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=numpy
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Numpy has a Nerq Trust Score of 76/100 (B+). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Numpy has a Trust Score of 76/100 (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.
Detailed Score Analysis
| Dimension | Score |
|---|---|
| Security | 90/100 |
| Maintenance | 100/100 |
| Popularity | 100/100 |
| Quality | 40/100 |
| Community | 35/100 |
Based on 5 dimensions. Data from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard.
What data does Numpy collect?
Numpy is a Python package maintained by Travis E. Oliphant et al.. It receives approximately 194,984,733 weekly downloads.
As a development package, Numpy does not directly collect end-user personal data. However, applications built with it may collect data depending on implementation. Privacy score: 80/100.
Review the package's dependencies for potential supply chain risks. Run your package manager's audit command regularly.
Full analysis: Numpy Privacy Report · Privacy review
Is Numpy secure?
Security score: 90/100. Numpy has 0 known vulnerabilities (CVEs) in the National Vulnerability Database. This is a clean record.
License information not available. Open-source packages allow independent security review of the source code.
Run your package manager's audit command (`npm audit`, `pip audit`, `cargo audit`) to check for known vulnerabilities in your dependency tree.
Full analysis: Numpy Security Report
Numpy Across Platforms
Same developer/company in other registries:
How we calculated this score
Numpy's trust score of 90.0/100 (A+) is computed from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. The score reflects 5 independent dimensions: security (90/100), maintenance (100/100), popularity (100/100), quality (40/100), community (35/100). Each dimension is weighted equally to produce the composite trust score.
Nerq analyzes over 7.5 million entities across 26 registries using the same methodology, enabling direct cross-entity comparison. Scores are updated continuously as new data becomes available.
Signals last measured on September 08, 2026. Data version: 1.0.
Full methodology documentation · Machine-readable data (JSON API)
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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.