Numerical Python in Astronomy and Astrophysics

This book provides a solid foundation in the Python programming language, numerical methods, and data analysis, all set in the context of astronomy and astrophysics. Readers are guided through function design, solving differential equations, and tackling classic dynamical problems. It enables students to learn programming through practical examples and motivates independent research. This book opens by emphasizing the growing role of computational techniques in modern astronomy and introduces Python as an ideal language for beginners. Key concepts such as Kepler’s laws and gravitational forces are used to illustrate basic calculations. This revised and expanded book includes a new chapter that offers a comprehensive introduction to machine learning in astronomy, expanding coverage of core methodologies and real-world applications. It introduces a wide range of modern concepts and techniques, including autoencoders, recurrent neural networks, and the fundamentals of large language models, demonstrating their relevance to contemporary astrophysical research. A major new feature of this edition is the extensive introduction of object-oriented programming throughout Chapter 4, providing readers with essential tools for developing structured, reusable, and scalable scientific software. The appendix on Python performance has been extended to include Numba, a tool for compiling Python code to machine code, demonstrated through optimization of the N-body solver. The new edition also includes new examples and exercises that deepen understanding and reflect current research challenges in astrophysics.