[SciPy-user] Any Books on SciPy?

John Hunter jdh2358@gmail....
Tue Feb 27 21:59:28 CST 2007


On 2/27/07, Robert Love <rblove@airmail.net> wrote:
> Are there any good, up to date books that people recommend for
> numerical work with Python?
>
> I see the book
>
>     Python Scripting for Computational Science
>     Hans Petter Langtangen
>
> Does anyone have opinions on this?  Is it current?  Are there better
> books?

I specifically do not recommend this book -- I own it but in my
opinion it is outdated and is more a collection of the author's
personal idioms than the current common practice in the scientific
python community.   For numerical work in python most people use

  * numpy - for array math.  The best documentation is Travis' online
book http://www.tramy.us.  For free, the numarray documentation is
excellent and the API is very similar for common use cases -
http://www.stsci.edu/resources/software_hardware/numarray/doc

 * scipy - there is no comprehensive printed documentation that I know
of.  The online help, site docs, and wiki are your best bet. -
http://www.scipy.org/Documentation

 * ipython - the enhanced python shell which is widely used in the
scientific computing community for interactive work.  Has special
modes to support numpy, scipy, matplotlib and distributed computing.
- http://ipython.scipy.org/moin/Documentation

  * matplotlib - 2D graphics, charts and the like.  ipython has a
'ipython -pylab' mode which loads matplotlib and numpy for a matlab
like environment .  http://matplotlib.sourceforge.net/tutorial.html
and http://matplotlib.sourceforge.net/users_guide_0.87.7.pdf

* Enthought Tool Suite - provides a comprehensive package for
scientific computing including the above modules and many others for
application development and more.  Provides 3D graphics through the
Mayavi2/VTK packages and 2D plotting through Chaco, and lots of tools
to facilitate wx based GUI development -  http://code.enthought.com/

There is a lot more, particularly for domain specific stiff, but these
links are good starting points.  Unfortunately, there is no
one-stop-shop for a guide to scientific computing in python - Travis'
documentation is the closest thing we have but it pretty much just
covers numpy which is *the* core package.  Fernando Perez and I have a
very brief and limited started guide covering multiple packages
(ipython, numpy, matplotlib, scipy, VTK) but I don't have the PDF
handy (Fernando, do you have the roadshow doc handy?).  Eric Jones and
Travis (authors of scipy) have some talk notes at
http://www.nanohub.org/resources/?id=99 but these are a bit out of
date.

JDH


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