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`frompyfunc` requires `dtype=object` when used with `accumulate` #4155

hpaulj opened this issue Dec 29, 2013 · 1 comment

`frompyfunc` requires `dtype=object` when used with `accumulate` #4155

hpaulj opened this issue Dec 29, 2013 · 1 comment


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@hpaulj hpaulj commented Dec 29, 2013

np.frompyfunc(lambda x,y:x+y,2,1).accumulate(np.arange(10))

raises an error:

ValueError: could not find a matching type for <lambda> (vectorized).accumulate, requested type has type code 'l'

accumulate only works if the dtype is set explicitly to object (either in the input, the out array, or as a kwarg).

np.frompyfunc(lambda x,y:x+y,2,1).accumulate(np.arange(10,dtype=object))

This behavior may be consistent with the note in its doc that The returned ufunc always returns PyObject arrays.. Also when vectorize uses a ufunc generated by this function, it converts the args to object type, and then converts the output to a specified (or deduced) otype.

However, is this error message required? A straight call of the ufunc returns an object array without any warning or error message:

np.frompyfunc(lambda x:2*x,1,1)(np.arange(4))
# array([0, 2, 4, 6], dtype=object)

Or is this just documentation issue? Does its __doc__ need an added note?

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@jakirkham jakirkham commented Dec 8, 2017

Probably does some casting under the hood (speculating as I have not looked). Maybe that is an option for accumulate too?

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