- Series: Texts in Computational Science and Engineering (Book 3)
- Paperback: 756 pages
- Publisher: Springer (November 23, 2010)
- Language: English
- ISBN-10: 3642093159
- ISBN-13: 978-3642093159
- Product Dimensions: 6 x 1.8 x 9 inches
- Shipping Weight: 2.8 pounds (View shipping rates and policies)
- Average Customer Review: 4.5 out of 5 stars See all reviews (13 customer reviews)
- Amazon Best Sellers Rank: #1,385,393 in Books (See Top 100 in Books)
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Python Scripting for Computational Science (Texts in Computational Science and Engineering)
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From the reviews of the second edition:
"This book addresses primarily a CSE (computational science and engineering) audience. … gives a clear and detailed account on the ways in which the surprisingly powerful Python language may aid the CSE community." (H. Muthsam, Monatshefte für Mathematik, Vol. 151 (4), 2007)
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Top customer reviews
The other third, however, is dedicated to GUI programming and integration with Scientific Software. It is full of very useful examples that are not difficult to replicate/modify for your needs.
It also addresses more advanced GUI programming using Canvas, C/C++ integration, efficiency, and other subjects I haven't read yet. If you ask me, it has everything I need. And man, when you find yourself without internet connection and *need* to make something work, books can really save you. True story.
5 stars for this one.
This book is really more of a "Grad Student's Guide to Everyday Python Usage". I imagine it would be very valuable to a mathematics Grad student without too much programming or shell experience, looking for an alternative to Matlab. However, there is very little "Computational Science" in this book. Do NOT expect a cookbook of high performance algorithm implementations.
The book is a very verbose 700+ pages, all in an unexciting academic LaTeX format. The author works through idiom after idiom for accomplishing different tasks in fairly stand-alone sub-sections without much of a feeling of conceptual "flow" between them. It sort of feels like reading through the author's personal lab notes that he took everytime he learned a new language feature or trick.
If you are an experienced programmer, you will quickly get impatient with the verbose presentation that emphasizes idioms and examples instead of fundamental concepts and syntax reference tables. But, if you are an experienced programmer, you are not the target audience for this book.
Here finally is a book with code examples that are very clear, are immediately useful to the serious programmer and filled with real life discourse on relative performance differences between Python and other languages that have a reputation for speed. There are clear examples of 'number crunching', producing images and even video animations, hooks into other scientific packages such as MathLab, etc.
If you are interested in really learning Python, want to come away from an hour or twos worth of coding experience with a module or two that you can use tomorrow and are not interested in code examples extolling Monty Python silliness, then this is the book for you.
While this book is about twice as expensive as many of my other Python books, I wish I had purchased this one first. Even though I've been using Python, seemingly every day, for two years, I kept finding nuggets in this book with what seemed to be every turn of the page. My focus right now is processing extremely large data sets of binary data but I'll soon be looking at image processing and I know I'll be reaching for this book over and over again. Don't hesitate! Just buy the book!