Is Scipy Safe?
Scipy — Nerq Trust Score 88.0/100 (A grade). Based on analysis of 2 trust dimensions, it is considered safe to use. Last updated: 2026-03-30.
Yes, Scipy is safe to use. Scipy is a Python package with a Nerq Trust Score of 88.0/100 (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: 2026-03-30. Machine-readable data (JSON).
Is Scipy safe?
YES — Scipy has a Nerq Trust Score of 88.0/100 (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 Scipy's trust score?
Scipy has a Nerq Trust Score of 88.0/100, 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 Scipy?
Scipy's strongest signal is popularity at 100/100. No known vulnerabilities have been detected. It meets the Nerq Verified threshold of 70+.
What is Scipy and who maintains it?
| Author | Unknown |
| Category | pypi |
| Source | N/A |
Scipy Across Platforms
Same developer/company in other registries:
Similar Pypi by Trust Score
Safety Guide: Scipy
What is Scipy?
Scipy is a Python package — Fundamental algorithms for scientific 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=scipy
Key Safety Concerns for Python packages
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Trust Assessment
Scipy has a Nerq Trust Score of 78/100 (B+) and meets Nerq trust threshold. This score is based on automated analysis of security, maintenance, community, and quality signals.
Key Takeaways
- Scipy has a Trust Score of 78/100 (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 | 85/100 |
| Maintenance | 60/100 |
Based on 5 dimensions. Data from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard.
What data does Scipy collect?
Scipy is a Python package maintained by Unknown. It receives approximately 79,719,475 weekly downloads. Licensed under Copyright (c) 2001-2002 Enthought, Inc. 2003, SciPy Developers. All rights reserved. .
As a development package, Scipy 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: Scipy Privacy Report · Privacy review
Is Scipy secure?
Security score: 90/100. Scipy has 0 known vulnerabilities (CVEs) in the National Vulnerability Database. This is a clean record.
Licensed under Copyright (c) 2001-2002 Enthought, Inc. 2003, SciPy Developers. All rights reserved. , allowing code inspection. 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: Scipy Security Report
How we calculated this score
Scipy's trust score of 88.0/100 (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 (85/100), 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 March 30, 2026. Data version: 1.0.
Full methodology documentation · Machine-readable data (JSON API)
Frequently Asked Questions
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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.