numpy.multiply can have at most 3 arguments (operands), is there any way to do more than 3?

Question

By the following example, I can confirm that multiply would only work with at most 3 arguments:

import numpy as np
w = np.asarray([2, 4, 6])
x = np.asarray([1, 2, 3])
y = np.asarray([3, 1, 2])
z = np.asarray([10, 10, 10])
np.multiply(w, x, y) # works
np.multiply(w, x, y, z) #failed

Here is the error message:

ValueError                                Traceback (most recent call last)
<ipython-input-14-9538812eb3b4> in <module>()
----> 1 np.multiply(w, x, y, z)

ValueError: invalid number of arguments

Is there any way to achieve multiply with more than 3 arguments? I don't mind to use other Python library.

Answer 1

You can use np.prod to calculate the product of array elements over a given axis , here it's (axis=0), ie multiply rows element-wise:

np.prod([w, x, y], axis=0)
# array([ 6,  8, 36])

np.prod([w, x, y, z], axis=0)
# array([ 60,  80, 360])

Answer 2

Actually multiply takes two arrays. It's a binary operation. The third is an optional out . But as a ufunc it has a reduce method which takes a list:

In [234]: x=np.arange(4)
In [235]: np.multiply.reduce([x,x,x,x])
Out[235]: array([ 0,  1, 16, 81])
In [236]: x*x*x*x
Out[236]: array([ 0,  1, 16, 81])
In [237]: np.prod([x,x,x,x],axis=0)
Out[237]: array([ 0,  1, 16, 81])

np.prod can do the same, but be careful with the axis parameter.

More fun with ufunc - accumulate:

In [240]: np.multiply.accumulate([x,x,x,x])
Out[240]: 
array([[ 0,  1,  2,  3],
       [ 0,  1,  4,  9],
       [ 0,  1,  8, 27],
       [ 0,  1, 16, 81]], dtype=int32)
In [241]: np.cumprod([x,x,x,x],axis=0)
Out[241]: 
array([[ 0,  1,  2,  3],
       [ 0,  1,  4,  9],
       [ 0,  1,  8, 27],
       [ 0,  1, 16, 81]], dtype=int32)