AstroML is a Python module designed for machine learning and data mining in the field of astronomy. It is built on popular libraries such as numpy, scipy, scikit-learn, matplotlib, and astropy, and is distributed under the 3-clause BSD license. The primary objective of AstroML is to provide astronomers and astrophysics researchers with a comprehensive and efficient toolkit for statistical data analysis.

It offers a growing collection of statistical and machine learning routines specifically developed for analyzing astronomical data in Python. Additionally, AstroML includes loaders for various open astronomical datasets and a wide range of examples that demonstrate how to analyze and visualize astronomical data effectively. By acting as a community repository, AstroML aims to offer a user-friendly and consistent interface for researchers to access freely available astronomical datasets.

It also welcomes contributions from the community via GitHub Pull Requests. AstroML was initially created to accompany the book Statistics, Data Mining, and Machine Learning in Astronomy written by Željko Ivezić, Andrew Connolly, Jacob Vanderplas, and Alex Gray. A second edition of the book was published in December 2019, featuring updates such as new sections on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation, as well as revisions throughout.

Whether you're an astronomy researcher or a student, AstroML can be a valuable tool to aid your data analysis and exploration in the field