data-analysis-patterns
Data Analysis Patterns
Expert guidance for making critical decisions in data analysis workflows, particularly around aggregation, recalculation, and maintaining analytical integrity.
When to Use This Skill
- Deciding whether to recalculate from raw data vs reuse aggregated data
- Changing category definitions in existing analyses
- Ensuring accuracy in publication-quality analyses
- Handling conflated features that need separation
- Optimizing analysis pipelines without sacrificing correctness
- Merging multi-source datasets with composite keys
- Handling DataFrame type conversion issues during enrichment
Core Patterns
1. Recalculating vs Reusing Aggregated Data
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