awesome-python-data-science

Why we included this project

This is a hand-curated index of Python data science libraries, grouped into sections that go beyond the obvious machine learning and deep learning buckets to include gradient boosting, kernel methods, feature engineering, and model explanation. That structure is the main draw: instead of running scattered GitHub searches, you get a map that separates general-purpose ML from specialized niches, with adjacent sections for data manipulation, visualization, and deployment when you are planning a full pipeline. It is a reference to browse and learn from rather than software you run, but it saves real time when you want to survey what Python offers for a particular data problem.