Numpy np.multiply vs *-Operator [duplicate]

Question

Is there any difference in using

import numpy as np  a, b = np.random([1024, 1024]), np.random([1024, 1024]) c = np.multiply(a, b) 

over

c = a * b 

or is the *-Operator on numpy-arrays simply overridden with np.multiply?

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Edit: This question is marked as duplicate because a question asks the same thing about the division operator (np.divide() vs /) and similar answers followed, but unless it is changed to "numpy arithmetic vs. python arithmetic" or something of the kind, it won't help people wondering the same thing as I did (about multiplication) and not being "clever" enough to assume a question about a related arithmetic operation (division) generalizes to all the basic arithmetic operations. To make it easier finding answers, I'd advocate for keeping this question as is.

Answer 1

There is no difference. However, the np.multiply function can take in additional, optional arguments, making it more versatile. See the docs.

Saying that * is overwritten with np.multiply would not be very precise. Generally, * maps to calls to the __mul__ and __rmul__ methods on the objects on which it acts. Thus, * is rather "overwritten" with np.ndarray.__mul__.

Answer 2

speed differences - none:

In [65]: timeit c = np.multiply(a,b) 4.95 ms ± 10.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) In [66]: timeit c = a*b 5.06 ms ± 180 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) 

For smaller arrays we might see differences due to a different calling stack, but with these, the actual computation dominates the time.

But as you can see from the docs, np.multiply is a ufunc with access to all the machinery that that implies.

For np.matrix objects, * is matrix product, np.multiply is element multiplication.