On Fri, Jun 24, 2011 at 7:10 PM, Charles R Harris <[email protected]> wrote: > > > On Fri, Jun 24, 2011 at 4:21 PM, Matthew Brett <[email protected]> > wrote: >> >> Hi, >> >> On Fri, Jun 24, 2011 at 10:09 PM, Benjamin Root <[email protected]> wrote: >> ... >> > Again, there are pros and cons either way and I see them very orthogonal >> > and >> > complementary. >> >> That may be true, but I imagine only one of them will be implemented. >> >> @Mark - I don't have a clear idea whether you consider the nafloat64 >> option to be still in play as the first thing to be implemented >> (before array.mask). If it is, what kind of thing would persuade you >> either way? >> > > Mark can speak for himself, but I think things are tending towards masks. > They have the advantage of one implementation for all data types, current > and future, and they are more flexible since the masked data can be actual > valid data that you just choose to ignore for experimental reasons. > > What might be helpful is a routine to import/export R files, but that > shouldn't be to difficult to implement. > > Chuck > > > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
Perhaps we should make a wiki page someplace summarizing pros and cons of the various implementation approaches? I worry very seriously about adding API functions relating to masks rather than having special NA values which propagate in algorithms. The question is: will Joe Blow Former R user have to understand what is the mask and how to work with it? If the answer is yes we have a problem. If it can be completely hidden as an implementation detail, that's great. In R NAs are just sort of inherent-- they propagate you deal with them when you have to via na.rm flag in functions or is.na. The other problem I can think of with masks is the extra memory footprint, though maybe this is no cause for concern. -W _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
