Carl wrote:
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.
Well, that may be true. And many applications spend most of their time
in fast libraries anyway (GUI/DB/app servers) etc. Nice examples are Boa
Constructor and Eric3 which are fully implemented in Python and run
comfortably fast, thanks to the WxWindows and Qt libraries.

But I really dont' swallow the argument. In the few years that I'm
working with Python, I've encountered on several occasions performance
bottlenecks. And often a solution through partial implementation in C or
improvements in the algorithm is just not feasible. To paraphrase Kent
Beck: in real life you make your program run, then work and then your
manager says you've run out of time. You just cannot make it run as fast
as you would like it as well.

In other words: it would be nice if Python on average would run faster
so the need for optimisation would lessen. Psyco is a nice option to
have, though not as clear cut as I thought it would be:

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.
So dogma dictates. And I've found it to be true on many occasions, if
not all. BUT, the famed Python Productivity Gain is very difficult to
quantify. And for me that's a BIG but. I'm trying to push Python within
my company. Nicely presented "performance benchmarks" go down well with
management, bevause those are quantities which are supposedly
understood. And Python does not come across very possitively in that



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