Matthew Brett writes: >> Maybe instead of np.NA, we could say np.IGNORE, which sort of conveys >> the idea that the entry is still there, but we're just ignoring it. Of >> course, that goes against common convention, but it might be easier to >> explain.
> I think Nathaniel's point is that np.IGNORE is a different idea than > np.NA, and that is why joining the implementations can lead to > conceptual confusion. This is how I see it: >>> a = np.array([0, 1, 2], dtype=int) >>> a[0] = np.NA ValueError >>> e = np.array([np.NA, 1, 2], dtype=int) ValueError >>> b = np.array([np.NA, 1, 2], dtype=np.maybe(int)) >>> m = np.array([np.NA, 1, 2], dtype=int, masked=True) >>> bm = np.array([np.NA, 1, 2], dtype=np.maybe(int), masked=True) >>> b[1] = np.NA >>> np.sum(b) np.NA >>> np.sum(b, skipna=True) 2 >>> b.mask None >>> m[1] = np.NA >>> np.sum(m) 2 >>> np.sum(m, skipna=True) 2 >>> m.mask [False, False, True] >>> bm[1] = np.NA >>> np.sum(bm) 2 >>> np.sum(bm, skipna=True) 2 >>> bm.mask [False, False, True] So: * Mask takes precedence over bit pattern on element assignment. There's still the question of how to assign a bit pattern NA when the mask is active. * When using mask, elements are automagically skipped. * "m[1] = np.NA" is equivalent to "m.mask[1] = False" * When using bit pattern + mask, it might make sense to have the initial values as bit-pattern NAs, instead of masked (i.e., "bm.mask == [True, False, True]" and "np.sum(bm) == np.NA") Lluis -- "And it's much the same thing with knowledge, for whenever you learn something new, the whole world becomes that much richer." -- The Princess of Pure Reason, as told by Norton Juster in The Phantom Tollbooth _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
