[Numpy-discussion] consensus (was: NA masks in the next numpy release?)
Sun Oct 30 00:02:14 CDT 2011
>> Here are my needs:
>> 1) How NAs are implemented cannot be end user visible. Having to pass
>> maskna=True is a problem. I suppose a solution is to set the flag to
>> true on every array inside of pandas so the user never knows (you
>> mentioned someone else had some other solution, i could go back and
>> dig it up?)
> I guess this would be the same with bitpatterns, in that the user
> would have to specify a custom dtype.
> Is it possible to add a bitpattern NA (in the NaN values) to the
> current floating point types, at least in principle? So that np.float
> etc would have bitpattern NAs without a custom dtype?
That is an interesting idea. It's essentially what people like Wes McKinney are doing now. However, the issue is going to be whether or not you do something special or not with the NA values in the low-level C function the dtype dispatches to. This is the reason for the special bit-pattern dtype.
I've always thought that requiring NA checks for code that doesn't want to worry about it would slow things down un-necessarily for those use-cases.
But, not dealing with missing data well is a missing NumPy feature.
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