Python Scripting for Computational
Science (Texts in Computational
Science and Engineering) by Hans
Petter Langtangen
Good Book
The goal of this book is to teach computational scientists how to develop
tailored, flexible, and human-efficient working environments built from
small programs (scripts) written in the easy-to-learn, high-level language
Python. The focus is on examples and applications of relevance to
computational scientists: gluing existing applications and tools, e.g. for
automating simulation, data analysis, and visualization; steering
simulations and computational experiments; equipping old programs with
graphical user interfaces; making computational Web applications; and
creating interactive interfaces with a Maple/Matlab-like syntax to numerical
applications in C/C++ or Fortran. In short, scripting with Python makes you
much more productive, increases the reliability of your scientific work and

lets you have more fun - on Unix, Windows and Macintosh. All the tools
and examples in this book are open source codes. The third edition is
compatible with the new NumPy implementation and features updated
information, correction of errors, and improved associated software tools.
Personal Review: Python Scripting for Computational Science
(Texts in Computational Science and Engineering) by Hans
Petter Langtangen
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!
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