Introduction to Python for Science and Engineering

David J. Pine (New York University, NY, USA)

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English

CRC Press
23 September 2024

Applied mathematics; Computer-aided design (CAD); Programming & scripting languages: general; Mathematical theory of computation

Series: Series in Computational Biophysics

Introduction to Python for Science and Engineering offers a quick and incisive introduction to the Python programming language for use in any science or engineering discipline. The approach is pedagogical and “bottom up,” which means starting with examples and extracting more general principles from that experience. No prior programming experience is assumed.

Readers will learn the basics of Python syntax, data structures, input and output, conditionals and loops, user-defined functions, plotting, animation, and visualization. They will also learn how to use Python for numerical analysis, including curve fitting, random numbers, linear algebra, solutions to nonlinear equations, numerical integration, solutions to differential equations, and fast Fourier transforms.

Readers learn how to interact and program with Python using JupyterLab and Spyder, two simple and widely used integrated development environments.

All the major Python libraries for science and engineering are covered, including NumPy, SciPy, Matplotlib, and Pandas. Other packages are also introduced, including Numba, which can render Python numerical calculations as fast as compiled computer languages such as C but without their complex overhead.

By: David J. Pine (New York University NY USA)
Imprint: CRC Press
Country of Publication: United Kingdom
Edition: 2nd edition
Dimensions: Height: 234mm, Width: 156mm,
Weight: 820g
ISBN: 9781032673905
ISBN 10: 1032673907
Series: Series in Computational Biophysics
Pages: 420
Publication Date: 23 September 2024
Audience: College/higher education , Professional and scholarly , Primary , Undergraduate
Format: Paperback
Publisher's Status: Active

1. Introduction 2. Launching Python 3. Integrated Development Environments 4. Strings, Lists, Arrays, and Dictionaries 5. Input and Output 6. Conditionals and Loops 7. Functions 8. Plotting 9. Numerical Routines: SciPy and NumPy 10. Python Classes: Encapsulation 11. Data Manipulation and Analysis: Pandas 12. Animation 13. Speeding up numerical calculations Appendix A Maintaining your installation Python Appendix B Glossary Appendix C Python Resources Index Index

David J. Pine has taught physics and chemical engineering for over 40 years at four different institutions: Cornell University (as a graduate student), Haverford College, UCSB, and NYU, where he is a Professor of Physics, Mathematics, and Chemical & Biomolecular Engineering. He has taught a broad spectrum of courses, including numerical methods. He does research on optical materials and in experimental soft-matter physics, which is concerned with materials such as polymers, emulsions, and colloids.