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Python Scripting for Computational Science (Texts in Computational Science and Engineering, 3)

3.9 3.9 out of 5 stars 20 ratings
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Numerous readers of the second edition have noti?ed me about misprints and possible improvements of the text and the associated computer codes. The resulting modi?cations have been incorporated in this new edition and its accompanying software. The major change between the second and third editions, however, is caused by the new implementation of Numerical Python, now called numpy. The new numpy package encourages a slightly di?erent syntax compared to the old Numeric implementation, which was used in the previous editions. Since Numerical Python functionality appears in a lot of places in the book, there are hence a huge number of updates to the new suggested numpy syntax, especially in Chapters 4, 9, and 10. The second edition was based on Python version 2.3, while the third edition contains updates for version 2.5. Recent Python features, such as generator expressions (Chapter 8.9.4), Ctypes for interfacing shared libraries in C (Chapter 5.2.2), the with statement (Chapter 3.1.4), and the subprocess module for running external processes (Chapter 3.1.3) have been exempli?ed to make the reader aware of new tools. Chapter 4.4.4 is new and gives a taste of symbolic mathematics in Python.
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  1. ISBN-10
    3642093159
  2. ISBN-13
    978-3642093159
  3. Publication date
    November 22, 2010
  4. Part of series
    Texts in Computational Science and Engineering
  5. Language
    English
  6. Dimensions
    6.1 x 1.76 x 9.25 inches
  7. Print length
    782 pages
  8. See all details
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Editorial Reviews

Review

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)

Product details

  • Publisher ‏ : ‎ Springer (November 22, 2010)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 782 pages
  • ISBN-10 ‏ : ‎ 3642093159
  • ISBN-13 ‏ : ‎ 978-3642093159
  • Item Weight ‏ : ‎ 2.37 pounds
  • Dimensions ‏ : ‎ 6.1 x 1.76 x 9.25 inches
  • Customer Reviews:
    3.9 3.9 out of 5 stars 20 ratings

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Hans Petter Langtangen

Hans Petter Langtangen

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Customer reviews

3.9 out of 5 stars
3.9 out of 5
20 global ratings

Top reviews from the United States

pafluxa
5.0 out of 5 stars A must have if you use python for scientific purposes.
Reviewed in the United States on August 27, 2013
Verified Purchase
This book is fantastic. The first third is dedicated to basic Numpy and "daily" operations that engineers and scientists encounter when working with Python, so it resembles a lot to any Numpy/Python book. Nothing "new".

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.
4 people found this helpful
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Patrick Faith
4.0 out of 5 stars a solid reference
Reviewed in the United States on December 19, 2010
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I bought this book, just for a couple of the chapters, but i found myself using more of this book then i expected, and reading all the chapters(even the fortran stuff). I found this book better then all my other "scientific python" books, in that my other books really built toyish apps. This book is meant for people doing production computational science work in python. It doesn't have much btw on super computer's programming and python, aside from a lot on how to integrate c/c++/fortan libraries(i.e. anyone doing major work in python probably is integrating to things like Tesla/Hadoop/mpi ... etc ... and the book didn't go to that level).
2 people found this helpful
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Alex H.
5.0 out of 5 stars Excellent Text and Reference!
Reviewed in the United States on June 12, 2010
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As an intermediate Python programmer, this excellent book has become my go to reference for useful intermediate and advanced techniques that I can locate and learn quickly. The writing is clear and not overly verbose. In addition to a wide array of numerical and scientific examples, the book is helpful for a wide range of programming issues, such as gluing together disparate legacy applications, interfacing to C++, regression testing numerical code, building GUI's, web programming, etc.
4 people found this helpful
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Amazon Customer
5.0 out of 5 stars Five Stars
Reviewed in the United States on April 25, 2018
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Exactly what i needed and help me out.
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John K
5.0 out of 5 stars A really great reference
Reviewed in the United States on September 7, 2016
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Great reference and well written with excellent examples.
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Wolves Zhao
5.0 out of 5 stars Great
Reviewed in the United States on December 6, 2014
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The book is great
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G. Jaouen
5.0 out of 5 stars *The* reference for folks who work with Matlab
Reviewed in the United States on July 26, 2009
I'm giving this book five stars because it was basically written for me. I don't mean that literally, of course. I say that because the usual methods of googling for answers and reading the manual do not work when you are trying to push the limits of what a tool is capable of doing. I do numerical computations for a variety of things -- finding patterns in large data sets, automating data collection and analysis, converting raw serial output into convenient CSV, plotting multidimensional datasets etc. Over the years, I have collected a large number of productivity habits with Matlab, which allows me to do ridiculously convoluted things in a short period of time. You just have to read the introduction of any Python manual to understand why I am switching from Matlab to Python. The problem is -- what will replace all these productivity habits? They need to be replaced with "Pythonic" habits, something that can take years of practice.

The beauty about Langtangen's book is that it runs through every one of those techniques. Instead of giving a basic example (what your google search would have provided) or a complete list of, ahem, useless techniques (what the manual would have provided), you get exactly what a seasoned data analyst needs to know to get moving with state-of-the-art commands. The author also discusses optimizations and alternatives in each chapter.

The book is also the best source for explaining *why* NumPy should be used by people working with large datasets. Folks love to create toolkits for Python, but some of these are a list of non-intuitive shortcuts that don't provide a substantial improvement over basic Python. Langtangen goes through the pain of explaining the benefits of the package (chapter 4.1.4), so that you can decide for yourself if NumPy is useful for your application.

I will not comment on the parts of the book that deal with C and FORTRAN integration because I leave that to more able programmers. I also will not comment on the extensive GUI building chapters because I do not build GUIs. I will point out, though, that I have derived full value out of this book simply by reading, and re-reading chapters 2, 3, 4 and 8. Some will argue that there is too much "basic Python" in these chapters for the whole to be considered advanced computational science -- my opinion is that even when the author describes "basic Python", his examples and intuition make it so that even one who has read a couple of reference books cover-to-cover will learn something about using "basic Python" to perform numerical analysis in a more efficient way. In fact, the book is a testament to doing really convoluted things in a really compact and elegant manner!
18 people found this helpful
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DickieCoon
5.0 out of 5 stars Outstanding introduction to Python and Numpy
Reviewed in the United States on May 15, 2010
I've bought what seems to (my wife) be every Python book out there and I can't tell you how sick I am of spam, spam, spam code! (trivial and obfuscated Python code examples with a common theme focused around one Monty Python skit or another...) Spam code seems to prevail in other Python books.

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!
8 people found this helpful
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Top reviews from other countries

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manuel
2.0 out of 5 stars Enlaces a las descargas de los programas no funcionan
Reviewed in Spain on December 31, 2018
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Debido a lo cual el libro se convierte en muy poco util y he tenido, muy a mi pesar, que devolverlo
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Partha Talukdar
1.0 out of 5 stars About the book
Reviewed in the United Kingdom on September 21, 2018
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I don't think it is a right choice for a beginner like me. In fact, I don't think the chapters are well organised and the examples for practice are not good enough.
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RiK0
3.0 out of 5 stars Interessante, mah
Reviewed in Italy on April 21, 2015
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Il libro e' interessante: sono molti gli argomenti trattati su come scrivere codice numerico, varie librerie sia in Python sia per legare Python a Fortran o C. Purtroppo il codice usa idiomi in generale vecchi e relativamente fuori standard, sembra davvero codice scritto da qualcuno che di mestiere fa altro. Il che fa pensare che questo libro sia pensato per chi di mestiere fa altro (come prevedibile dal titolo). Per queste persone vale sicuramente la pena di essere preso per la carrellata di tecnologie di interesse che fa. Purtroppo tende a sviluppare una libreria custom di utilita' per fare esempi e simili, il che e' di utilita' limitata per chi poi il codice lo deve scrivere e usare (tipicamente una libreria introdotta in un libro e non affermatasi per selezione naturale della comunita' open source e' parecchio sotto gli standard di quello che si trova in giro).
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Loïc
4.0 out of 5 stars Un des meilleurs bouquins sur les usages scientifiques avancés de python, tout en permettant une initiation aux débutants
Reviewed in France on May 4, 2013
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Un des meilleurs bouquins sur les usages scientifiques avancés de python, tout en permettant une initiation aux débutants.

Et je compare avec 8 autres titres (dont peu en français d'un niveau valable), sans compter 4 titres supplémentaires sur les traitements geospatial, texte, PyQt, matplotlib et visualisation scientifique en général.
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