Is Numpy Safe?
Numpy — Nerq Trust Score 90.0/100 (A+ grade). Based on analysis of 2 trust dimensions, it is considered safe to use. Last updated: 2026-04-01.
Yes, Numpy is safe to use. Numpy is a Python package with a Nerq Trust Score of 90.0/100 (A+), based on 3 independent data dimensions. It is recommended for production use. Security: 90/100. Popularity: 100/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-04-01. Machine-readable data (JSON).
Is Numpy safe?
YES — Numpy has a Nerq Trust Score of 90.0/100 (A+). It meets Nerq's trust threshold with strong signals across security, maintenance, and community adoption. Recommended for production use — review the full report below for specific considerations.
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. It meets the Nerq Verified threshold of 70+.
What is Numpy and who maintains it?
| Author | Travis E. Oliphant et al. |
| Category | pypi |
| 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 packages
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Trust Assessment
Numpy has a Nerq Trust Score of 76/100 (B+) and meets Nerq trust threshold. This score is based on automated analysis of security, maintenance, community, and quality signals.
Key Takeaways
- Numpy has a Trust Score of 76/100 (B+).
- Recommended for use — passes trust threshold.
- Always verify independently using the Nerq API.
Detailed Score Analysis
| Dimension | Score |
|---|---|
| Security | 90/100 |
| Privacy | 80/100 |
| Reliability | 90/100 |
| Transparency | 50/100 |
| Maintenance | 60/100 |
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), privacy (80/100), reliability (90/100), transparency (50/100), maintenance (60/100). 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.
This page was last reviewed on April 01, 2026. Data version: 1.0.
Full methodology documentation · Machine-readable data (JSON API)
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Disclaimer: Nerq trust scores are automated assessments based on publicly available signals. They are not endorsements or guarantees. Always conduct your own due diligence.