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Python's simplicity philosophy

Alex Martelli aleax at aleax.it
Fri Nov 14 12:02:25 EST 2003 
Douglas Alan wrote:
   ...
>> That is non-trivial to most, based on my experience in explaining it
>> to other people (which for the most part have been computational
>> physicists, chemists, and biologists).
> 
> I find this truly hard to believe.  APL was a favorite among
> physicists who worked at John's Hopkins Applied Physics Laboratory
> where I lived for a year when I was in high school, and you wouldn't

Interesting.  I worked for many years in an environment where physicists
doing research could freely choose between APL and Fortran (IBM Research),
and while there was a hard-core of maybe 10%-15% of them who'd _never_
leave APL for any reason whatsoever, an overwhelmingly larger number
of physicist was at least as keen on Fortran.  I have no hard data on
the subject, but it appears to me that Fortran has always been way more
popular than APL among physicists as a whole.

> thing.  In fact, people seemed to like reduce() and friends -- people
> seemed to think it was a much more fun way to program, rather than
> using boring ol' loops.

...while most physicists I worked with were adamant that they wanted
to continue coding loops and have the _compiler_ vectorize them or
parallelize them or whatever.  Even getting them to switch to Linpack
etc from SOME of those loops was a battle at first, though as I recall 
the many advantages did eventually win them over.

Anyway, computational scientists using Python should be using Numeric
(if they aren't, they're sadly misguided).  Numeric's ufuncs ARE the
right way to do the equivalent of APL's +/ (which is quite a different
beast than ANYusercodedfunction/ would be...), and show clear and
obvious advantages in so doing:

[alex at lancelot tmp]$ timeit.py -c -s'import operator; xs=range(999)'
'x=reduce(operator.add, xs)'
1000 loops, best of 3: 290 usec per loop

[alex at lancelot tmp]$ timeit.py -c -s'xs=range(999)' 's=sum(xs)'
10000 loops, best of 3: 114 usec per loop

[alex at lancelot tmp]$ timeit.py -c -s'import Numeric as N;
xs=N.arrayrange(999)' 'x=N.add.reduce(xs)'
100000 loops, best of 3: 9.3 usec per loop

Now *THAT* is more like it: 10+ times FASTER than sum, rather than
2+ times SLOWER!  Of course, you do have to use it right: in this
snippet, if you initialize xs wrongly...:

[alex at lancelot tmp]$ timeit.py -c -s'import Numeric as N; xs=range(999)'
'x=N.add.reduce(xs)'
100 loops, best of 3: 2.1e+03 usec per loop

...then you can say goodbye to performance, as you see.  But when
used skilfully, Numeric (or its successor numarray, I'm sure -- I
just don't have real experience with the latter yet) is just what
numerically heavy computations in Python require.


Alex
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