Pine D. Introduction to Python for Science and Engineering 2ed 2025

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Pine D. Introduction to Python for Science and Engineering 2ed 2025

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Textbook in PDF format 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. The aim of the second edition remains the same as the first: to provide science and engineering students a practical introduction to technical programming in Python. This new edition adds nearly 100 pages of new material. A significant disadvantage is that Python programs can be slower than compiled languages like C. For large-scale simulations and other demanding applications, there can be a considerable speed penalty in using Python. In these cases, C, C++, or Fortran are recommended, although intelligent use of Python’s array processing tools in the NumPy module can significantly speed up Python code. Alternatively, several new tools have recently appeared that can be used to speed up certain numerical computations in Python signifcantly, often by one or two orders of magnitude. 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. Preface Introduction Launching Python Integrated Development Environments Strings, Lists, Arrays, and Dictionaries Input and Output Conditionals and Loops Functions Plotting Numerical Routines: SciPy and NumPy Python Classes: Encapsulation Data Manipulation and Analysis: Pandas Animation Speeding Up Numerical Calculations Appendix A Maintaining Your Python Installation Appendix B Glossary Appendix C Python Resources