A student's Guide to Python for Physical Modeling

Jesse M Kinder

ISBN 10: 0691223653 / ISBN 13: 9780691223650

Published by Princeton University Press, 2021

About this Item

Abnutzung / Risse - leicht; Gebrochener Buchrücken / Seiten oder Softcover umgeknickt. "Python is a computer programming language that is rapidly gaining popularity throughout the sciences. A Student's Guide to Python for Physical Modeling aims to help you, the student, teach yourself enough of the Python programming language to get started with physical modeling. You will learn how to install an open-source Python programming environment and use it to accomplish many common scientific computing tasks: importing, exporting, and visualizing data; numerical analysis; and simulation. No prior programming experience is assumed."--.

Seller Inventory # 5525849e-d60f-4645-a0b6-6973c99a736a

Bibliographic Details

• Title: A student's Guide to Python for Physical Modeling
• Publisher: Princeton University Press
• Publication Date: 2021
• Language: English
• ISBN 10: 0691223653
• ISBN 13: 9780691223650
• Binding: Softcover
• Edition: 2nd Edition
• Condition: Fine
• Seller catalogs:
  • Physics

About this title

Synopsis

A fully updated tutorial on the basics of the Python programming language for science students

Python is a computer programming language that has gained popularity throughout the sciences. This fully updated second edition of A Student's Guide to Python for Physical Modeling aims to help you, the student, teach yourself enough of the Python programming language to get started with physical modeling. You will learn how to install an open-source Python programming environment and use it to accomplish many common scientific computing tasks: importing, exporting, and visualizing data; numerical analysis; and simulation. No prior programming experience is assumed.

This guide introduces a wide range of useful tools, including:

• Basic Python programming and scripting
• Numerical arrays
• Two- and three-dimensional graphics
• Animation
• Monte Carlo simulations
• Numerical methods, including solving ordinary differential equations
• Image processing

Numerous code samples and exercises―with solutions―illustrate new ideas as they are introduced. This guide also includes supplemental online resources: code samples, data sets, tutorials, and more. This edition includes new material on symbolic calculations with SymPy, an introduction to Python libraries for data science and machine learning (pandas and sklearn), and a primer on Python classes and object-oriented programming. A new appendix also introduces command line tools and version control with Git.

About the Author

Jesse M. Kinder is associate professor of physics at the Oregon Institute of Technology. Philip Nelson is professor of physics at the University of Pennsylvania. His books include From Photon to Neuron (Princeton), Physical Models of Living Systems, and Biological Physics.