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

Juan Nunez-Iglesias jni.soma at gmail.com
Sat Mar 26 18:10:00 EDT 2016


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!!!


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)), makingnp.bincountreturn an array of the same type as theweightsparameter.This is an important deviation from current behavior, which always castsweightstodouble, and always returns adoublearray, so I would like to hear what others think about the worthiness of this.Main discussion points:
> np.bincountnow works with complex weights (yay!), I guess this should be a pretty uncontroversial enhancement.
> The return is of the same type asweights, 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 along, as we do innp.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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