[SciPy-user] Pros and Cons of Python verses other array environments

David Finlayson david.p.finlayson at gmail.com
Sat Sep 30 02:05:49 CDT 2006


I'm a lurker on this list, but this thread peaked my interests. I am a
hydrographer (coastal mapping) not a computer scientist and I don't have
much training in numerical computation (I did take the token applied math
class in Matlab of course). So, my perspective on numeric Python is as an
end user in a production environment. To me (and most of the people I
support), data analysis environments like Matlab are black boxes. Maybe I am
not the numeric Python target audience.

We depend extensively on Matlab to do data analysis and plotting on our
team. The vast majority of the scientists I work with struggle with
programming and hand coding an FFT in FORTRAN would be impossible (for
example). Matlab, or something like it is a necessary tool. Why not numeric
Python?

In a nutshell, it still looks like alpha software compared to Matlab.
Documentation is not ready for end users (and not professionally published).
Some of the numeric libraries have been around for ages, but that only adds
to the confusion because there are numerous packages spread all over the
Internet with a chain of dependencies that adds still more confusion. It
still looks like a patchwork quilt rather than an organized system. Finally,
most production environments outside of academia use Windows or maybe OSX.
That means that (a) there is no compiler, its batteries included or its
dead; and (b) there is a real need for an integrated environment because
Emacs doesn't come installed on Windows.

Python itself is making great headway on Windows at least. In my field
(mapping), the big commercial vendor included Python as its macro language,
so there has been an explosion of interest in python scripting. Recent
publishing of IronPython for .NET was fully supported by Microsoft and there
is every reason to believe that it will be a popular way to script and
control the .NET framework. So, I think that average engineers and
scientists are aware of Python the language and would be receptive to a data
analysis package in Python so long as it was polished and well done. For
example, the R statistical language has done a good job of packaging up R
for Windows (I wish it were as well integrated into Gnome).

I am not trying to take pot shots at numeric python here or FOSS or Linux. I
use all of these personally. I just can't convince myself that this is a
safe recommendation for folks I support.

-- 
David Finlayson
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