Optimizes Pandas and NumPy code for high-performance data processing, memory efficiency, and vectorization.
Python Data Reviewer is a specialized Claude Code skill designed to audit data science code for performance bottlenecks and memory leaks. It analyzes projects using Pandas and NumPy to identify inefficient patterns like row-based iteration or sub-optimal data types, providing actionable recommendations for vectorization, memory management, and robust data validation. This skill is essential for data engineers and scientists looking to scale ETL pipelines and streamline Jupyter notebook workflows by implementing modern, vectorized implementation patterns.
Key Features
01Identifies and replaces slow iterrows() and apply() patterns with vectorized operations.
020 GitHub stars
03Automatically detects data science contexts via pandas/numpy dependencies and .ipynb files.
04Recommends NumPy broadcasting and pre-allocation strategies for high-performance computing.
05Provides memory footprint optimization through intelligent dtype and category recommendations.
06Audits ETL pipelines for data validation, null handling, and schema consistency.
Use Cases
01Performing specialized code reviews for data-intensive projects using large-scale DataFrames.
02Refactoring legacy Jupyter notebooks into production-ready, memory-efficient Python code.
03Optimizing slow data processing scripts to reduce ETL runtime and compute costs.
