Wed, 23 Jul 2014 22:13:33 +0100 Nathaniel Smith :

On Wed, Jul 23, 2014 at 9:57 PM, Robert Kern wrote:

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That's perhaps what you want, but numpy has never claimed to do this.

... except in np.where, which promises to return indices but actually returns arrays of longs and thus doesn't work with large arrays on Windows. I know this is a bug that can be fixed without changing the size of np.int, but it goes to show that even core functionality in NumPy gets it wrong.

This is true, but it's not very compelling on its own -- "big as a pointer" is a much much more useful property than "big as a long". The only real reason this made sense in the first place is the equivalence between Python int and C long, but even that is gone now with Python 3. IMO at this point backcompat is really the only serious reason for keeping int32 as the default integer type in win64. But of course this is a pretty serious concern...

Hear, hear. The C type long is only useful as an "at least 32-bit" integer, but on the platforms that NumPy targets, int is also at least that large. The only real benefit of long is that it makes porting more interesting </sarcasm>. If you have intp and a bunch of explicitly-sized integer types, you don't need an additional type that behaves like a long *except* for backward compat. The Go people got this right; they only have explicitly-sized integer types and an int type the size of a pointer [1]. [1] http://golang.org/doc/go1.1#int