Python is commonly used in web services, where retrieving information from a database and transmitting the results via slow network connections are what takes up the most time, not processing the data in between with Python.

I don't buy that. I've had enough people relate Python's roots in the sysad tool belt and it's application in processing large data sets to believe that it's intended use case could be that limited.

And you shouldn't :)

Python is used just about everywhere for just about everything (sometimes properly, sometimes poorly). While there are a lot of web sites that run Python, there are also countless other applications that use it that are unrelated to the web. See Scipy as an example.

That said, the earlier poster mentioning that many people are using Python for IO bound processes is not too far a stretch. Why else would Twisted Python exist, and why would the new Tulip async IO stuff be developed?

I'm not denying that IO is important to a certain class of Python programs too; but I still can't see what leads to the conclusion that IO bound programs are the primary use case for Python. There are enough evented and async io libraries being built for just about every platform right now, that doesn't make IO the center of any of them.

And then there is the GIL. So if you want to squeeze more performance out of your CPU bound task. You need to use the multiprocess module, because Python threading does not work well for CPU bound tasks.

Since you are talking about CPU-bound tasks, it is relevant that numpy array operations release the GIL.

True, you would have to write or use a Python extension written in C or C++.