[Numpy-discussion] Counting array elements
verveer at embl-heidelberg.de
Mon Oct 25 09:44:04 CDT 2004
On 25 Oct 2004, at 18:19, Stephen Walton wrote:
> On Mon, 2004-10-25 at 10:26 +0200, Peter Verveer wrote:
>> On 25 Oct 2004, at 04:17, Stephen Walton wrote:
>>> I don't think we need sumall. The methods and the functions should
>>> simply work the same way. If one wants sumall, use A.flat.sum() or,
>>> you can't use the methods or attributes on your old version of
>> I think this may be inefficient, because ravel and flat may make a
>> of the data. Also I think using flat/ravel in such a way is plain ugly
>> and a complex way to do it.
> You may be right about the copying, I couldn't say. I don't think
> sum(ravel(A)) looks any worse than sum(sum(sum(A))) for a rank 3 array,
> but ugly is in the eye of the beholder.
It does not look worse, I agree with that! But I would argue it should
have been sum(A) in the first place to sum over al axes... The sumall
would not have been needed, and summing over one (or a sub-set) axis
could have been implemented as a an optional argument to sum().
>> In my opinion functions that calculate a statistic like sum
>> should return the total in the first place, rather then over a single
> It depends on the data. I use rank-2 arrays which are images and are
> therefore homogeneous. Even there, though, I often want the sum of all
> rows or all columns. For heterogeneous data (e.g., columns of
> Y's as a function of X), the present sum() makes sense. In other
> we will always need ways to sum over just one dimension and over all
> dimensions. By analogy with MATLAB (I'm guessing), sum() in Numeric
> numarray does a one-D sum.
I agree it is a useful feature, and it should still be possible to do
that using an optional axis argument, even better I would love to be
able to sum over several axes in one go, I find the one-dimensional
character of reduce limiting, but I digress. In any case, I suppose we
will stick with the current behaviour for backwards compatibility.
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