[Numpy-discussion] feedback request: proposal to add masks to the core ndarray
Charles R Harris
Fri Jun 24 08:33:02 CDT 2011
On Fri, Jun 24, 2011 at 6:30 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 ?
> 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.
Interesting thought. Doing that could be handled by adding r-dtypes, as in
r-float32, r-int, etc. However, adding so many dtypes with different
behaviors could make for a messy implementation, whereas masks would be
uniform across types.
-------------- next part --------------
An HTML attachment was scrubbed...
More information about the NumPy-Discussion