Your hands-on guide to using Python for scientific computing!

Python for Engineering and Scientific Computing

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$54.99

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511 pages, , Print edition paperback
ISBN 978-1-4932-2559-0
511 pages,
E-book formats: EPUB, PDF, online
ISBN 978-1-4932-2560-6
511 pages, , Print edition paperback
E-book formats: EPUB, PDF, online
ISBN 978-1-4932-2561-3
It’s finally here—your guide to Python for engineers and scientists, by an engineer and scientist! Get to know your development environments and the key Python modules you’ll need: NumPy, SymPy, SciPy, Matplotlib, and VPython. Understand basic Python program structures and walk through practical exercises that start simple and increase in complexity as you work your way through the book. With information on statistical calculations, Boolean algebra, and interactive programming with Tkinter, this Python guide belongs on every scientist’s shelf!
  • A practical guide to Python for nonprogrammers
  • Work with NumPy, SymPy, SciPy, Matplotlib, and VPython
  • Automate numerical calculations, create simulations and visualizations, perform statistical analysis, and more
About the Book About the E-book 511 pages, paperback, 1 in. Reference book format 7 x 10 in. Printed black and white on 50# offset paper from sustainable sources. Reader-friendly serif font (TheAntiquaB 9.5 Pt.). One-column layout.Find the German book here. E-book in full color. PDF (19 MB) and EPUB files (9 MB) for download, DRM-free with personalized digital watermark. Copy and paste, bookmarks, and print-out permitted. Table of contents, in-text references, and index fully linked. Including online book edition in dedicated reader application.

In this book, you'll learn about:

  1. Python for Scientists

    You already know how to solve problems in your field. In this guide you’ll find the tools you need to map your existing calculations with Python.

  2. Python Modules

    Learn how NumPy, SymPy, SciPy, Matplotlib, and VPython can help you with your scientific work. From solving linear equations and creating animations to using discrete Fourier transformations to reconstruct a noisy signal, you’ll find the practical advice you need.

  3. Python in Action

    Start simple by sizing a shaft and then move on to increasingly advanced exercises: animating a pendulum, simulating a rolling element bearing defect, creating a predator-prey model, and more. For every exercise, review the complete source code, with extensive comments, and analyze the output.

Highlights include:

  • Program structures
  • NumPy
  • Matplotlib
  • SymPy
  • SciPy
  • VPython
  • Tkinter
  • Numerical calculations
  • Statistical calculations
  • Boolean algebra

Dr. Veit Steinkamp studied electrical engineering and German to become a teacher and pass on his knowledge at vocational schools and technical colleges.

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