Introduction to Python for Science and Engineering - Pine, David J.; - Prospero Internet Bookshop
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Introduction to Python for Science and Engineering

 
Edition number: 2
Publisher: CRC Press
Date of Publication:
 
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Short description:

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.

Long description:

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.

Table of Contents:

1. Introduction


2. Launching Python


3. Integrated Development Environments


4. Strings, Lists, Arrays, and Dictionaries


5. Input and Output


6. Conditionals and Loops


7. Functions


8. Plotting


9. Numerical Routines: SciPy and NumPy


10. Python Classes: Encapsulation


11. Data Manipulation and Analysis: Pandas


12. Animation


13. Speeding up numerical calculations


Appendix A Maintaining your installation Python


Appendix B Glossary


Appendix C Python Resources


Index Index