
A Student's Guide to Python for Physical Modeling
Second Edition
Princeton University Press
2nd Edition
Published on 3. August 2021
Book
Paperback/Softback
240 pages
978-0-691-22365-0 (ISBN)
Description
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.
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.
More details
Edition
2nd School edition
Language
English
Place of publication
New Jersey
United States
Target group
College/higher education
Edition type
School edition
Product notice
Paperback (trade)
Illustrations
5 color illus.
Dimensions
Height: 254 mm
Width: 206 mm
Thickness: 22 mm
Weight
555 gr
ISBN-13
978-0-691-22365-0 (9780691223650)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

E-Book
09/2024
2nd Edition
Bonnier Books UK
€32.49
Available for download

E-Book
08/2021
2nd Edition
University Press of Mississippi
€31.99
Available for download
Persons
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.