What is the difference between vectorize and frompyfunc in numpy?
Both seem very similar. What is a typical use case for each of them?
Edit: As JoshAdel indicates, the class vectorize seems to be built upon frompyfunc. (see the source). It is still unclear to me whether frompyfunc may have any use case that is not covered by vectorize...
As JoshAdel points out, vectorize wraps frompyfunc. Vectorize adds extra features:
Edit: After some brief benchmarking, I find that vectorize is significantly slower (~50%) than frompyfunc for large arrays. If performance is critical in your application, benchmark your use-case first.
`
>>> a = numpy.indices((3,3)).sum(0)
>>> print a, a.dtype
[[0 1 2]
[1 2 3]
[2 3 4]] int32
>>> def f(x,y):
"""Returns 2 times x plus y"""
return 2*x+y
>>> f_vectorize = numpy.vectorize(f)
>>> f_frompyfunc = numpy.frompyfunc(f, 2, 1)
>>> f_vectorize.__doc__
'Returns 2 times x plus y'
>>> f_frompyfunc.__doc__
'f (vectorized)(x1, x2[, out])\n\ndynamic ufunc based on a python function'
>>> f_vectorize(a,2)
array([[ 2, 4, 6],
[ 4, 6, 8],
[ 6, 8, 10]])
>>> f_frompyfunc(a,2)
array([[2, 4, 6],
[4, 6, 8],
[6, 8, 10]], dtype=object)
`
I'm not sure what the different use cases for each is, but if you look at the source code (/numpy/lib/function_base.py), you'll see that vectorize wraps frompyfunc. My reading of the code is mostly that vectorize is doing proper handling of the input arguments. There might be particular instances where you would prefer one vs the other, but it would seem that frompyfunc is just a lower level instance of vectorize.
frompyfunc seems to be lower level than vectorize. The question remains, though, of whether there are cases where you would prefer to use frompyfunc instead of vectorize?
Although both methods provide you a way to build your own ufunc, numpy.frompyfunc method always returns a python object, while you could specify a return type when using numpy.vectorize method
frompyfuncproduces functions that consciously disregard thedtypeargument and return an array ofobjects. As the documentation explains, "The returned ufunc always returns PyObject arrays". There is an easy and ingenious workaround: submit an array of desired type asoutargument. Thevectorizefunction, on the contrary, allows to specify the output type of the ufunc withotypesargument, but it is supposed to be slow and hence fairly useless, compared to using nested lists.