[Numpy-discussion] Missing data again

Pierre Haessig pierre.haessig@crans....
Tue Mar 6 07:48:00 CST 2012

Hi Mark,

I went through the NA NEP a few days ago, but only too quickly so that
my question is probably a rather dumb one. It's about the usability of
bitpatter-based NAs, based on your recent post :

Le 03/03/2012 22:46, Mark Wiebe a écrit :
> Also, here's a thought for the usability of NA-float64. As much as
> global state is a bad idea, something which determines whether
> implicit float dtypes are NA-float64 or float64 could help. In
> IPython, "pylab" mode would default to float64, and "statlab" or
> "pystat" would default to NA-float64. One way to write this might be:
> >>> np.set_default_float(np.nafloat64)
> >>> np.array([1.0, 2.0, 3.0])
> array([ 1.,  2.,  3.], dtype=nafloat64)
> >>> np.set_default_float(np.float64)
> >>> np.array([1.0, 2.0, 3.0])
> array([ 1.,  2.,  3.], dtype=float64)

Q: Is is an *absolute* necessity to have two separate dtypes "nafloatNN"
and "floatNN" to enable NA bitpattern storage ?

From a potential user perspective, I feel it would be nice to have NA
and non-NA cases look as similar as possible. Your code example is
particularly striking : two different dtypes to store (from a user
perspective) the exact same content ! If this *could* be avoided, it
would be great...

I don't know how the NA machinery is working R. Does it works with a
kind of "nafloat64" all the time or is there some type inference
mechanics involved in choosing the appropriate type ?


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