Paperback. 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. Introduction to Python for Science and Engineering offers an incisive introduction to the Python programming language for use in any science or engineering discipline. The approach is pedagogical, which means starting with examples and extracting more general principles from that experience. No prior programming experience is assumed. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

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Detalles bibliogr�ficos

T�tulo: Introduction to Python for Science and Engineering (Paperback)
Editor: Taylor & Francis Ltd, London
Fecha de publicaci�n: 2024
Idioma: Ingl�s
ISBN 10: 1032673907
ISBN 13: 9781032673905
Encuadernaci�n: Paperback
Edici�n: 2� Edici�n
Estado: new

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Sinopsis

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

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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.

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