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Название: A Student's Guide to Python for Physical Modeling, 2nd Edition
Автор: Jesse M. Kinder, Philip Nelson
Издательство: Princeton University Press
Год: 2021
Страниц: 241
Язык: английский
Формат: pdf (true)
Размер: 10.3 MB
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. 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.
Автор: Jesse M. Kinder, Philip Nelson
Издательство: Princeton University Press
Год: 2021
Страниц: 241
Язык: английский
Формат: pdf (true)
Размер: 10.3 MB
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. 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.