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