[Numpy-discussion] Missing data again
Charles R Harris
Wed Mar 7 12:48:05 CST 2012
On Wed, Mar 7, 2012 at 11:21 AM, Lluís <email@example.com> wrote:
> Charles R Harris writes:
> > One inconvenience I have run into with the current API is that is should
> > 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
> make bit-patterns and masks indistinguishable from the POV of the array
> What's the workflow that leads you to un-ignore specific elements?
Because they are not 'unknown', just (temporarily) 'ignored'. This might be
the case if you are experimenting with what happens if certain data is left
out of a fit. The current implementation tries to handle both these case,
and can do so, I would just like the 'ignored' use to be more convenient
than it is.
> > So maybe two types of masks (different payloads), or an additional flag
> > be helpful.
> Do you mean different NA values? If that's the case, I think it was taken
> 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
> the main design points was to make them indistinguishable in the common
No, the mask as currently implemented is eight bits and can be extended to
handle different mask values, aka, payloads.
> I think the name was "parametrized dtypes".
They don't interest me in the least. But that is a whole different area of
> > 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.
Suppose I receive a data set, say an hdf file, that also includes a mask.
I'd like to load the data and apply the mask directly without doing
data[mask] = np.NA
Do you mean here that this is too cumbersome to use?
> >>> a[a < 5] = np.NA
> (obviously oversimplified example where everything looks sufficiently
> simple :))
Mostly speed and memory.
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