Data science pack

Starter pack

Analysis-ready rules for the scientific Python stack: pandas, NumPy, scikit-learn, and Matplotlib.

5 items · curated by waxmark

Pandas

This guide outlines definitive best practices for writing high-performance, maintainable, and robust pandas code, focusing on modern patterns and avoiding common pitfalls.

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Numpy

This guide provides definitive, actionable best practices for writing high-performance, maintainable, and correct NumPy code, emphasizing vectorization, explicit dtypes, and modern GPU acceleration.

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Scikit Learn

Definitive guidelines for writing robust, maintainable, and performant scikit-learn code, emphasizing consistent preprocessing, API adherence, and data leakage prevention.

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Matplotlib

This guide outlines definitive best practices for writing clean, performant, and maintainable matplotlib code, emphasizing the object-oriented API and modern data science workflows.

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Python

"Python best practices and patterns for modern software development with Flask and SQLite"