"Nine Language Performance Round-up: Benchmarking Math & File I/O"

I think this is an unfair comparison! I wouldn't dream of developing a
numerical application in Python without using prebuilt numerical libraries
and data objects such as dictionaries and lists.

I have been experimenting with numerical algorithms in Python with a heavy
use of the Numeric module. My experience is that Python is quite fast in
comparison with (and sometimes as fast as) traditional languages such as C
or C++.

The greatest advantage of Python is the great increase in productivity and
the generation of a much smaller number of bugs due to the very clean and
compact structure Python invites you to produce. Sometimes it amazes me how
fast I can produce a working algorithm in Python. The step from an
algorithmic outline on a paper to a working code is very short. The
interactive nature of the Python console invites numerical experimentation
and data exploration. This wasn't mentioned in the article, what a pity!


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