Description
Unlock the power of programming with 'A Student's Guide to Python for Physical Modeling'. This essential resource provides a comprehensive tutorial tailored for science students, enabling you to self-learn Python programming effectively. As Python's popularity continues to soar in scientific fields, understanding its application for physical modeling becomes crucial. This fully updated second edition not only covers the foundational aspects of Python but also offers you a hands-on approach to scientific computing. You'll learn to install an open-source Python environment and tackle common tasks like data visualization, numerical analysis, and simulation. With no prior programming experience necessary, this user-friendly guide is your gateway to mastering:
- Basic Python programming and scripting
- Numerical arrays and data manipulation
- Stunning two- and three-dimensional graphics
- Engaging animations and simulations
- Monte Carlo methods for probabilistic modeling
- Numerical techniques for solving ordinary differential equations
- Image processing tools
Designed with numerous code samples and practice exercises, this guide will enhance your understanding of Python. Online resources, including tutorials and data sets, complement your learning experience. New features of this edition include symbolic calculations with SymPy, introductions to data science libraries like pandas and sklearn, and insights into object-oriented programming. The appendix also offers guidance on command line tools and Git for version control, ensuring you're equipped with vital skills for today's tech-driven landscape. Note that shipping for this item is free and may take up to 6 weeks for delivery. Once your order is placed, it cannot be canceled. Dive into the world of Python programming and elevate your physical modeling skills today!
Note: Shipping for this item is free. Please allow up to 6 weeks for delivery. Once your order is placed, it cannot be cancelled.
Condition: BRAND NEW
ISBN: 9780691223650
Format: Trade paperback (US)
Year: 2021
Publisher: Princeton University Press
Description:
A fully updated tutorial on the basics of the Python programming language for science studentsPython 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.
- Basic Python programming and scripting
- Numerical arrays and data manipulation
- Stunning two- and three-dimensional graphics
- Engaging animations and simulations
- Monte Carlo methods for probabilistic modeling
- Numerical techniques for solving ordinary differential equations
- Image processing tools
Designed with numerous code samples and practice exercises, this guide will enhance your understanding of Python. Online resources, including tutorials and data sets, complement your learning experience. New features of this edition include symbolic calculations with SymPy, introductions to data science libraries like pandas and sklearn, and insights into object-oriented programming. The appendix also offers guidance on command line tools and Git for version control, ensuring you're equipped with vital skills for today's tech-driven landscape. Note that shipping for this item is free and may take up to 6 weeks for delivery. Once your order is placed, it cannot be canceled. Dive into the world of Python programming and elevate your physical modeling skills today!
Note: Shipping for this item is free. Please allow up to 6 weeks for delivery. Once your order is placed, it cannot be cancelled.
Condition: BRAND NEW
ISBN: 9780691223650
Format: Trade paperback (US)
Year: 2021
Publisher: Princeton University Press
Description:
A fully updated tutorial on the basics of the Python programming language for science studentsPython 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.