This repository is a curated list of Python resources for data science, targeting practitioners who want to leverage Python for data analysis, machine learning, and visualization. It provides a comprehensive overview of libraries, tutorials, and tools across various domains, aiming to streamline the data science workflow.

How It Works

The list is organized thematically, covering core libraries like pandas and scikit-learn, alongside specialized tools for areas such as big data processing (Spark, Dask), natural language processing (spaCy, NLTK), computer vision, time series analysis, and recommender systems. It also includes resources for configuration management, environment setup, and visualization.

Quick Start & Requirements

This is a curated list, not a runnable project. No installation or execution commands are provided. The resources listed have varying requirements, typically including Python and specific libraries.

Highlighted Details

• Extensive coverage of pandas alternatives and parallelization libraries (Polars, Vaex, Modin).
• Detailed sections on feature engineering, selection, and dimensionality reduction techniques.
• Broad collection of visualization libraries, from basic plotting to interactive dashboards.
• Comprehensive resources for machine learning, including deep learning frameworks, NLP, and computer vision.

Maintenance & Community

The repository is maintained by r0f1. Contributions are welcomed via pull requests or issues for adding new resources or updating existing ones.

Licensing & Compatibility

The repository itself is not software and does not have a license. The linked resources have their own licenses, which should be checked individually.

Limitations & Caveats

As a curated list, it does not provide direct functionality. The quality and maintenance status of individual linked resources may vary. Some links might be outdated or point to projects that are no longer actively developed.