- Series: Texts in Computational Science and Engineering (Book 3)
- Hardcover: 756 pages
- Publisher: Springer; 3rd edition (February 13, 2009)
- Language: English
- ISBN-10: 3540739157
- ISBN-13: 978-3540739159
- Product Dimensions: 6.4 x 1.3 x 9.4 inches
- Shipping Weight: 2.8 pounds (View shipping rates and policies)
- Average Customer Review: 13 customer reviews
- Amazon Best Sellers Rank: #1,197,887 in Books (See Top 100 in Books)
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Python Scripting for Computational Science (Texts in Computational Science and Engineering) 3rd Edition
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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
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!
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.
The order of the chapters seems somewhat odd at first. In the end it looks like a well designed build-up of complexity, with only a little price to pay (some tiny bits of repetition and to experienced users sometimes unclear where to find what). Although knowledge of Python is not necessary, there is not a lot of space in the book wasted on the basics (previous experience in programming is, in fact, helpful). The book is oriented towards scientists and engineers, with a lot of code ready in C/C++/Fortran who need to glue that code together and possibly do some additional numerical or analysis work on the data. It is also perfectly suited for people who want to use only Python for their (numerical and analysis) work.
- Basic Python (clean, clear, quick, some more than usual emphasis on handy I/O functionality)
- Advanced Python (clear, many more useful extras like regular expressions, parsing command line options, iterators, etc. than in many other books, good examples, missing topic: decorators)
- NumPy and numeric analysis (extensive, very good, could have had more on SciPy, some emphasis on older/obsolete packages like Numeric, ScientificPython, not enough on e.g. Matplotlib)
- Interfacing with C/C++/Fortran through arrays (very useful and well explained)
- GUI programming (clear, maybe a bit too much of advanced GUI programming, which could have been figured out by interested users by themselves, seems like too much emphasis for this topic)
- cgi programming/web interfaces (nice little extra gadget in my opinion, most scientists won't necessarily use this)
One feature that highly surprised me was the preferred use of 'from name import *', which I think is a bad habit. At some point it is even presented as useful when the same function name gets redefined in the global namespace, which I think is not something you want people to do. Other than that: great book and definitely worth its price!