[Numpy-discussion] What should be the value of nansum of nan's?
Charles R Harris
Thu Apr 29 11:56:48 CDT 2010
On Wed, Apr 28, 2010 at 11:56 AM, T J <email@example.com> wrote:
> On Mon, Apr 26, 2010 at 10:03 AM, Charles R Harris
> <firstname.lastname@example.org> wrote:
> > On Mon, Apr 26, 2010 at 10:55 AM, Charles R Harris
> > <email@example.com> wrote:
> >> Hi All,
> >> We need to make a decision for ticket #1123 regarding what nansum should
> >> return when all values are nan. At some earlier point it was zero, but
> >> currently it is nan, in fact it is nan whatever the operation is. That
> >> consistent, simple and serves to mark the array or axis as containing
> >> nans. I would like to close the ticket and am a bit inclined to go with
> >> current behaviour although there is an argument to be made for returning
> >> for the nansum case. Thoughts?
> > To add a bit of context, one could argue that the results should be
> > consistent with the equivalent operations on empty arrays and always be
> > non-nan.
> > In : nansum()
> > Out: nan
> > In : sum()
> > Out: 0.0
> This seems like an obvious one to me. What is the spirit of nansum?
> Return the sum of array elements over a given axis treating
> Not a Numbers (NaNs) as zero.
> Okay. So NaNs in an array are treated as zeros and the sum is
> performed as one normally would perform it starting with an initial
> sum of zero. So if all values are NaN, then we add nothing to our
> original sum and still return 0.
> I'm not sure I understand the argument that it should return NaN. It
> is counter to the *purpose* of nansum. Also, if one wants to
> determine if all values in an array are NaN, isn't there another way?
> Let's keep (or make) those distinct operations, as they are definitely
> distinct concepts.
It looks like the consensus is that zero should be returned. This is a
change from current behaviour and that bothers me a bit. Here are some other
In : nanmax([nan])
In : nanargmax([nan])
In : nanargmax()
So it looks like the current behaviour is very much tilted towards nans as
missing data flags. I think we should just leave that as is with perhaps a
note in the docs to that effect. The decision here should probably
accommodate the current users of these functions, of which I am not one. If
we leave the current behaviour as is then I think the rest of the nan
functions need fixes to return nan for empty sequences as nansum is the only
one that currently does that.
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