On Jun 24, 2011, at 4:44 PM, Robert Kern wrote: > On Fri, Jun 24, 2011 at 09:35, Robert Kern <robert.k...@gmail.com> wrote: >> On Fri, Jun 24, 2011 at 09:24, Keith Goodman <kwgood...@gmail.com> wrote: >>> On Fri, Jun 24, 2011 at 7:06 AM, Robert Kern <robert.k...@gmail.com> wrote: >>> >>>> The alternative proposal would be to add a few new dtypes that are >>>> NA-aware. E.g. an nafloat64 would reserve a particular NaN value >>>> (there are lots of different NaN bit patterns, we'd just reserve one) >>>> that would represent NA. An naint32 would probably reserve the most >>>> negative int32 value (like R does). Using the NA-aware dtypes signals >>>> that you are using NA values; there is no need for an additional flag. >>> >>> I don't understand the numpy design and maintainable issues, but from >>> a user perspective (mine) nafloat64, etc sounds nice. >> >> It's worth noting that this is not a replacement for masked arrays, >> nor is it intended to be the be-all, end-all solution to missing data >> problems. It's mostly just intended to be a focused tool to fill in >> the gaps where masked arrays are less convenient for whatever reason; >> e.g. where you're tempted to (ab)use NaNs for the purpose and the >> limitations on the range of values is acceptable. Not every dtype >> would have an NA-aware counterpart. I would suggest just nabool, >> nafloat64, naint32, nastring (a little tricky due to the flexible >> size, but doable), and naobject. Maybe a couple more, if we get >> requests, like naint64 and nacomplex128. > > Oh, and nadatetime64 and natimedelta64.
So, if I understand correctly: if my array has a nafloat type, it's an array that supports missing values and it will always have a mask, right ? And just viewing an array as a nafloat dtyped one would make it an 'array-with-missing-values' ? That's pretty elegant. I like that. Now, how will masked values represented ? Different masked values from one dtype to another ? What would be the equivalent of something like `if a[0] is masked` that we have know? _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
