On 2011-06-24 13:59, Nathaniel Smith <n...@pobox.com> wrote: > On Thu, Jun 23, 2011 at 5:56 PM, Benjamin Root<ben.r...@ou.edu> wrote: >> Lastly, I am not entirely familiar with R, so I am also very curious about >> what this magical "NA" value is, and how it compares to how NaNs work. >> Although, Pierre brought up the very good point that NaNs woulldn't work >> anyway with integer arrays (and object arrays, etc.). > Since R is designed for statistics, they made the interesting decision > that *all* of their core types have a special designated "missing" > value. At the R level this is just called "NA". Internally, there are > a bunch of different NA values -- for floats it's a particular NaN, > for integers it's INT_MIN, for booleans it's 2 (IIRC), etc. (You never > notice this, because R will silently cast a NA of one type into NA of > another type whenever needed, and they all print the same.) > > Because any array can contain NA's, all R functions then have to have > some way of handling this -- all their integer arithmetic knows that > INT_MIN is special, for instance. The rules are basically the same as > for NaN's, but NA and NaN are different from each other (because one > means "I don't know, could be anything" and the other means "you tried > to divide by 0, I *know* that's meaningless"). > > That's basically it. > > -- Nathaniel
Would the use of R's system for expressing "missing values" be possible in numpy through a special flag ? Any given numpy array could have a boolean flag (say "na_aware") indicating that some of the values are representing a missing cell. If the exact same system is used, interaction with R (through something like rpy2) would be simplified and more robust. L. PS: In R, dividing one by zero returns +/-Inf, not NaN. 0/0 returns NaN. _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
