Is Python Docx Safe?
Python Docx — Nerq Trust Score 73.2/100 (B grade). Score based on 2 independent trust signals. Last analyzed: 2026-08-11
Python Docx is a Python package with a Nerq Trust Score of 73.2/100 (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 Python Docx safe?
Trust Score Breakdown — Python Docx has a Nerq Trust Score of 73.2/100 (B). Measured across 2 independent trust signals (as of 2026-08-11).
What is Python Docx's trust score?
Python Docx has a Nerq Trust Score of 73.2/100, 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 Python Docx?
Python Docx's strongest signal is popularity at 100/100. No known vulnerabilities have been detected.
What is Python Docx and who maintains it?
| Author | Unknown |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Python Docx
What is Python Docx?
Python Docx is a Python package — Create, read, and update Microsoft Word .docx files..
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=python-docx
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Python Docx has a Nerq Trust Score of 73/100 (B). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Python Docx has a Trust Score of 73/100 (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.