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

Steven H. Rogers steve at shrogers.com
Sat Sep 30 09:50:28 CDT 2006

G'day David:

I believe the weaknesses you list are well understood and are being 
addressed.  I don't think it fair to say that SciPy looks like alpha 
software.  It may not have a slick interface suitable for the most naive 
users, but it is quite powerful.

You'll have to decide about the "safety" of recommending Python/NumPy/SciPy 
for your organization.  I introduced Python into an engineering organization 
   about six years ago as a scripting language for a piece of equipment we 
were developing and am just beginning to introduce NumPy.  It's been a bumpy 
road, but Python has gained wider adoption than I expected.  Many of our 
engineers can write an FFT in C or assembly, but balked at Python.  Once 
they really used it, most grew to like it and only drop down to lower level 
code when necessary.

I'd suggest looking for a niche requirement that SciPy can fill as a 
starting point.  If it does well there, you can expand to other niches.  If 
not, the risk should be minimal.


David Finlayson wrote:
> 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

Steven H. Rogers, Ph.D., steve at shrogers.com
Weblog: http://shrogers.com/weblog
"He who refuses to do arithmetic is doomed to talk nonsense."
-- John McCarthy

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