Does this skill support Jupyter Notebooks?

Yes, it automatically detects .ipynb files and can review data processing logic within notebook cells for efficiency and best practices.

What is the difference between this and the general python-reviewer?

The general reviewer focuses on PEP8 and logic, while this skill specifically targets high-performance data operations, NumPy broadcasting, and Pandas-specific bottlenecks.

How does it improve data processing speed?

It scans for Python-level loops over DataFrames and suggests vectorized alternatives using NumPy or built-in Pandas methods which run at C-speed.

Can it help resolve memory errors in Pandas?

Absolutely. It suggests memory-efficient dtypes (like int8 or float32), category conversions for low-cardinality strings, and chunking strategies for large datasets.

Python Data Reviewer

Name: Python Data Reviewer
Author: physics91

byphysics91

Ciencia de Datos y ML

Optimizes Pandas and NumPy code for high-performance data processing, memory efficiency, and vectorization.

Acerca de

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.

Características Principales

Identifies and replaces slow iterrows() and apply() patterns with vectorized operations.
0 GitHub stars
Automatically detects data science contexts via pandas/numpy dependencies and .ipynb files.
Recommends NumPy broadcasting and pre-allocation strategies for high-performance computing.
Provides memory footprint optimization through intelligent dtype and category recommendations.
Audits ETL pipelines for data validation, null handling, and schema consistency.

Casos de Uso

Performing specialized code reviews for data-intensive projects using large-scale DataFrames.
Refactoring legacy Jupyter notebooks into production-ready, memory-efficient Python code.
Optimizing slow data processing scripts to reduce ETL runtime and compute costs.

Acerca de

Características Principales

Identifies and replaces slow iterrows() and apply() patterns with vectorized operations.
0 GitHub stars
Automatically detects data science contexts via pandas/numpy dependencies and .ipynb files.
Recommends NumPy broadcasting and pre-allocation strategies for high-performance computing.
Provides memory footprint optimization through intelligent dtype and category recommendations.
Audits ETL pipelines for data validation, null handling, and schema consistency.

Casos de Uso

Performing specialized code reviews for data-intensive projects using large-scale DataFrames.
Refactoring legacy Jupyter notebooks into production-ready, memory-efficient Python code.
Optimizing slow data processing scripts to reduce ETL runtime and compute costs.