[Numpy-discussion] feedback request: proposal to add masks to the core ndarray
Fri Jun 24 11:48:56 CDT 2011
On Fri, Jun 24, 2011 at 7:30 AM, Laurent Gautier <firstname.lastname@example.org> wrote:
> On 2011-06-24 13:59, Nathaniel Smith <email@example.com> wrote:
> > On Thu, Jun 23, 2011 at 5:56 PM, Benjamin Root<firstname.lastname@example.org> wrote:
> >> Lastly, I am not entirely familiar with R, so I am also very curious
> >> 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 ?
I think that's something R2Py would have to handle in its compatibility
later. I'd like to first make the system within NumPy work well for NumPy,
interoperability at the low ABI level like this is a bit too restricting, I
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.
> PS: In R, dividing one by zero returns +/-Inf, not NaN. 0/0 returns NaN.
> NumPy-Discussion mailing list
-------------- next part --------------
An HTML attachment was scrubbed...
More information about the NumPy-Discussion