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
- Define requirements
- Choose tools
- Implement modular
- Test thoroughly
- Document decisions
- Monitor results
Common Pitfalls
- Ignoring constraints
- Skipping standards
- Poor alignment
- Inadequate testing
- No documentation
- Over-engineering
- No rollback
- No monitoring
- No scalability
- No validation
Verification Checklist
- [ ] Requirements validated
- [ ] Standards applied
- [ ] Design reviewed
- [ ] Tests defined
- [ ] Docs complete
- [ ] Monitoring configured
- [ ] Rollback planned
- [ ] Security checked
- [ ] Deploy verified
- [ ] Stakeholder approved
Related skills
Prompt Engineering
Data & AI
Prompt engineering best practices and templates to maximize AI outputs.
claudeCursorWindsurf+1beginner
289
78
896
Data Visualization
Data & AI
Generates data visualizations and charts tailored to your data.
claudeCursorWindsurfintermediate
198
56
726
RAG Architecture Setup
Data & AI
Setup guide for RAG (Retrieval-Augmented Generation) architectures.
claudeCursorWindsurfadvanced
167
51
688