Difference between frompyfunc and vectorize in numpy

Question

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...

Answer 1

As JoshAdel points out, vectorize wraps frompyfunc. Vectorize adds extra features:

• Copies the docstring from the original function
• Allows you to exclude an argument from broadcasting rules.
• Returns an array of the correct dtype instead of dtype=object

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) 

`