[Numpy-discussion] Missing data again

[Lluís]

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

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