For a long time now, numpy's memmap has me puzzled by its behavior. When I use memmap straightforward on a file it seems to work fine, but whenever I try to do a memmap using a dtype it seems to gobble up the whole file into memory. This, of course, makes the use of memmap futile. I would expect that the result of such an operation would give me a true memmap and that the data would be converted to dtype on the fly. I've seen this behavior in version version 1.04, 1.1.1 and still in 1.2.1. I'm working on Windows haven't tried it on Linux. Am I doing something wrong? Are my expectations wrong? Or is this an issue somewhere deeper in numpy? I looked at the memmap.py and it seems to me that most of the work is delegated to numpy.ndarray.__new__. Something wrong there maybe? Can somebody help please? Thanks! Regards, Wim Bakker