pandas-ai vs stable-hash — Trust Score Comparison
Side-by-side trust comparison of pandas-ai and stable-hash. Scores based on security, compliance, maintenance, popularity, and ecosystem signals.
pandas — Nerq Trust Score 80.8/100 (A-). httpx — Nerq Trust Score 80.8/100 (A-). Nearly identical overall trust.
Detailed Score Analysis
| Dimension | pandas | httpx |
|---|---|---|
| Security | 90/100 | 90/100 |
| Maintenance | 100/100 | 100/100 |
| Popularity | 100/100 | 100/100 |
| Quality | 65/100 | 65/100 |
| Community | 35/100 | 35/100 |
Five-dimension Nerq trust breakdown (registries: pypi / pypi). Scored equally weighted across security, maintenance, popularity, quality, community.
Detailed Metric Comparison
| Metric | pandas-ai | stable-hash |
|---|---|---|
| Trust Score | 66.8/100 | 56.8/100 |
| Grade | B- | D |
| Stars | 23,207 | 0 |
| Category | data | uncategorized |
| Security | 0 | N/A |
| Compliance | 82 | 100 |
| Maintenance | 1 | N/A |
| Documentation | 0 | N/A |
| EU AI Act Risk | minimal | N/A |
| Verified | No | No |
Verdict
pandas-ai leads with a trust score of 66.8/100 compared to stable-hash's 56.8/100 (a 10.0-point difference). Both agents should be evaluated based on your specific requirements.
Detailed Analysis
Security
Security scores measure dependency vulnerabilities, CVE exposure, and security practices. pandas-ai scores 0 and stable-hash scores N/A on this dimension.
Maintenance & Activity
Activity scores reflect how actively each project is maintained. pandas-ai: 1, stable-hash: N/A.
Documentation
Documentation quality is evaluated based on README, API docs, and example coverage. pandas-ai: 0, stable-hash: N/A.
Community & Adoption
pandas-ai has 23,207 GitHub stars while stable-hash has 0. pandas-ai has significantly broader community adoption, which typically means more Stack Overflow answers, more third-party tutorials, and faster ecosystem development.
When to Choose Each Tool
Choose pandas-ai if you need:
- Higher overall trust score — more reliable for production use
- More actively maintained with faster release cadence
- Larger community (23,207 vs 0 stars)
Choose stable-hash if you need:
- Consider if it better fits your specific use case
Switching from pandas-ai to stable-hash (or vice versa)
When migrating between pandas-ai and stable-hash, consider these factors:
- API Compatibility: pandas-ai (data) and stable-hash (uncategorized) serve different categories, so migration may require significant refactoring.
- Security Review: Run a security audit after migration. Check the pandas-ai safety report and stable-hash safety report for known issues.
- Testing: Ensure your test suite covers all integration points before switching in production.
- Community Support: pandas-ai has 23,207 stars and stable-hash has 0. Larger communities typically mean better Stack Overflow answers and migration guides.
Related Pages
Frequently Asked Questions
Related Comparisons
Last updated: 2026-05-31 | Data refreshed weekly
Disclaimer: Nerq trust scores are automated assessments based on publicly available signals. They are not endorsements or guarantees. Always conduct your own due diligence.