[Numpy-discussion] Make np.bincount output same dtype as weights

[Jaime Fernández del Río]

On Sat, Mar 26, 2016 at 11:10 PM, Juan Nunez-Iglesias <jni.soma at gmail.com>
wrote:

> Just to clarify, this will only affect weighted bincounts, right? I can't
> tell you in how many places my code depends on the return type being
> integer!!!
>

Indeed! Unweighted bincounts still return, as all counting operations, a
np.intp array. Sorry for the noise!

Jaime



>
>
> On 27 Mar 2016, 7:16 AM +1100, Jaime Fernández del Río <
> jaime.frio at gmail.com>, wrote:
>
> Hi all,
>
> I have just submitted a PR (#7464
> <https://github.com/numpy/numpy/pull/7464>) that fixes an enhancement
> request (#6854 <https://github.com/numpy/numpy/issues/6854>), making
> np.bincount return an array of the same type as the weights parameter.
> This is an important deviation from current behavior, which always casts
> weights to double, and always returns a double array, so I would like to
> hear what others think about the worthiness of this.  Main discussion
> points:
>
>    - np.bincount now works with complex weights (yay!), I guess this
>    should be a pretty uncontroversial enhancement.
>    - The return is of the same type as weights, which means that small
>    integers are very likely to overflow.  This is exactly what #6854
>    requested, but perhaps we should promote the output for integers to a
>    long, as we do in np.sum?
>    - Boolean arrays stay boolean, and OR, rather than sum, the weights.
>    Is this what one would want? If we decide that integer promotion is the way
>    to go, perhaps booleans should go in the same pack?
>    - This new implementation currently supports all of the reasonable
>    native types, but has no fallback for user defined types.  I guess we
>    should attempt to cast the array to double as before if no native loop can
>    be found? It would be good to have a way of testing this though, any
>    thoughts on how to go about this?
>    - Does a behavior change like this require some deprecation period?
>    What would that look like?
>    - I have also added broadcasting of weights to the full size of list,
>    so that one can do e.g. np.bincount([1, 2, 3], weights=2j) without
>    having to tile the single weight to the size of the bins list.
>
> Any other thoughts are very welcome as well!
>
> Jaime
>
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