Charles R Harris writes: [...] > One inconvenience I have run into with the current API is that is should be > easier to clear the mask from an "ignored" value without taking a new view or > assigning known data.
AFAIR, the inability to directly access a "mask" attribute was intentional to make bit-patterns and masks indistinguishable from the POV of the array user. What's the workflow that leads you to un-ignore specific elements? > So maybe two types of masks (different payloads), or an additional flag could > be helpful. Do you mean different NA values? If that's the case, I think it was taken into account when implementing the current mechanisms (and was also mentioned in the NEP), so that it could be supported by both bit-patterns and masks (as one of the main design points was to make them indistinguishable in the common case). I think the name was "parametrized dtypes". > The process of assigning masks could also be made a bit easier than using > fancy indexing. I don't get what you mean here, sorry. Do you mean here that this is too cumbersome to use? >>> a[a < 5] = np.NA (obviously oversimplified example where everything looks sufficiently simple :)) 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