I propose a simple idea *for the long term* for generalizing Mark's proposal, that I hope may perhaps put some people behind Mark's concrete proposal in the short term.
If key feature missing in Mark's proposal is the ability to distinguish between different reason for NA-ness; IGNORE vs. NA. However, one could conceive wanting to track a whole host of reasons: homework_grades = np.asarray([2, 3, 1, EATEN_BY_DOG, 5, SICK, 2, TOO_LAZY]) Wouldn't it be a shame to put a lot of work into NA, but then have users to still keep a seperate "shadow-array" for stuff like this? a) In this case the generality of Mark's proposal seems justified and less confusing to teach newcomers (?) b) Since Mark's proposal seems to generalize well to many NAs (there's 8 bits in the mask, and millions of available NaN-s in floating point), if people agreed to this one could leave it for later and just go on with the proposed idea. I don't think we should scetch out the above in more detail now, I don't want to distract, I just thought it something to consider to resolve the current situation... FWIW, Dag Sverre _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
