Fondamentaux du nettoyage de données avec Pandas

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name: pandas-cleaning-fundamentals description: "Use when applying pandas data cleaning fundamentals." version: 1.0.0 author: Hermes Agent license: MIT platforms: [linux, macos, windows] metadata: hermes: tags: [python, pandas, data-cleaning, pandas-data-manipulation] related_skills: [general]

Overview

Clean data with Pandas.

When to Use

• Design and implement
• Apply best practices
• Optimize performance
• Troubleshoot issues

Key Approaches

1. Define requirements
2. Choose tools
3. Implement modular
4. Test thoroughly
5. Document decisions
6. Monitor results

Common Pitfalls

1. Ignoring constraints
2. Skipping standards
3. Poor alignment
4. Inadequate testing
5. No documentation
6. Over-engineering
7. No rollback
8. No monitoring
9. No scalability
10. No validation

Verification Checklist

• [ ] Requirements validated
• [ ] Standards applied
• [ ] Design reviewed
• [ ] Tests defined
• [ ] Docs complete
• [ ] Monitoring configured
• [ ] Rollback planned
• [ ] Security checked
• [ ] Deploy verified
• [ ] Stakeholder approved