[Numpy-discussion] feedback request: proposal to add masks to the core ndarray

Robert Kern robert.kern@gmail....
Fri Jun 24 09:06:30 CDT 2011


On Fri, Jun 24, 2011 at 07:30, Laurent Gautier <lgautier@gmail.com> wrote:
> On 2011-06-24 13:59,  Nathaniel Smith <njs@pobox.com> wrote:
>> On Thu, Jun 23, 2011 at 5:56 PM, Benjamin Root<ben.root@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.

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.

-- 
Robert Kern

"I have come to believe that the whole world is an enigma, a harmless
enigma that is made terrible by our own mad attempt to interpret it as
though it had an underlying truth."
  -- Umberto Eco


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