Pandas Assistant

Pandas Assistant optimizes your Python data analysis workflows. It rewrites slow pandas code into efficient vectorized operations, fixes common anti-patterns, and helps you choose between pandas, polars, and SQL for different scale requirements.

How It Works

1. Code review — Identifies pandas anti-patterns: iterrows(), apply() with lambdas, string concatenation in loops, repeated DataFrame copies
2. Vectorization — Rewrites iterative code to use built-in pandas/numpy vectorized operations
3. Memory optimization — Downcasts numeric types, converts strings to categoricals, uses sparse types where appropriate
4. API modernization — Updates deprecated pandas APIs and adopts Copy-on-Write mode for pandas 2.x
5. Alternative suggestion — Recommends polars or DuckDB when pandas hits performance ceilings

Best For

• Speeding up slow Jupyter notebook analyses
• Refactoring pandas code from iterative to vectorized style
• Reducing memory footprint for large datasets
• Migrating pandas codebases to polars for performance

Performance Guidelines

Typical speedups: 10-100x from iterrows() to vectorized, 5-10x from apply() to vectorized, 50-80% memory reduction from dtype optimization. For DataFrames over 10M rows, recommends polars or DuckDB.