Hi, On Sat, Oct 29, 2011 at 10:02 PM, Travis Oliphant <oliph...@enthought.com> wrote: >>> >>> Here are my needs: >>> >>> 1) How NAs are implemented cannot be end user visible. Having to pass >>> maskna=True is a problem. I suppose a solution is to set the flag to >>> true on every array inside of pandas so the user never knows (you >>> mentioned someone else had some other solution, i could go back and >>> dig it up?) >> >> I guess this would be the same with bitpatterns, in that the user >> would have to specify a custom dtype. >> >> Is it possible to add a bitpattern NA (in the NaN values) to the >> current floating point types, at least in principle? So that np.float >> etc would have bitpattern NAs without a custom dtype? > > That is an interesting idea. It's essentially what people like Wes McKinney > are doing now. However, the issue is going to be whether or not you do > something special or not with the NA values in the low-level C function the > dtype dispatches to. This is the reason for the special bit-pattern dtype. > > I've always thought that requiring NA checks for code that doesn't want to > worry about it would slow things down un-necessarily for those use-cases.
Right - now that the caffeine has run through my system adequately, I have a few glasses of wine to disrupt my logic and / or social skills but: Is there any way you could imagine something like this?: In [3]: a = np.arange(10, dtype=np.float) In [4]: a.flags Out[4]: C_CONTIGUOUS : True F_CONTIGUOUS : True OWNDATA : True WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False MAYBE_NA : False In [5]: a[0] = np.NA In [6]: a.flags Out[6]: C_CONTIGUOUS : True F_CONTIGUOUS : True OWNDATA : True WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False MAYBE_NA : True Obviously extension writers would have to keep the flag maintained... Sorry if that doesn't make sense, I do not claim to be in full possession of my faculties, See you, Matthew _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
