Elementwise multiplication of several arrays in Python Numpy
Solution 1
Your fault is in not reading the documentation:
numpy.multiply(x1, x2[, out])
multiply
takes exactly two input arrays. The optional third argument is an output array which can be used to store the result. (If it isn't provided, a new array is created and returned.) When you passed three arrays, the third array was overwritten with the product of the first two.
Solution 2
For anyone stumbling upon this, the best way to apply an element-wise multiplication of n np.ndarray
of shape (d, )
is to first np.vstack
them and apply np.prod
on the first axis:
>>> import numpy as np
>>>
>>> arrays = [
... np.array([1, 2, 3]),
... np.array([5, 8, 2]),
... np.array([9, 2, 0]),
... ]
>>>
>>> print(np.prod(np.vstack(arrays), axis=0))
[45 32 0]
Solution 3
Yes! Simply as doing * to np.arrays
import numpy as np
a=np.array([2,9,4])
b=np.array([3,4,5])
c=np.array([10,5,8])
d=a*b*c
print(d)
Produce:
[ 60 180 160]
seb
Updated on July 27, 2022Comments
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seb almost 2 years
Coding some Quantum Mechanics routines, I have discovered a curious behavior of Python's NumPy. When I use NumPy's multiply with more than two arrays, I get faulty results. In the code below, i have to write:
f = np.multiply(rowH,colH) A[row][col]=np.sum(np.multiply(f,w))
which produces the correct result. However, my initial formulation was this:
A[row][col]=np.sum(np.multiply(rowH, colH, w))
which does not produce an error message, but the wrong result. Where is my fault in thinking that I could give three arrays to numpy's multiply routine?
Here is the full code:
from numpy.polynomial.hermite import Hermite, hermgauss import numpy as np import matplotlib.pyplot as plt dim = 3 x,w = hermgauss(dim) A = np.zeros((dim, dim)) #build matrix for row in range(0, dim): rowH = Hermite.basis(row)(x) for col in range(0, dim): colH = Hermite.basis(col)(x) #gaussian quadrature in vectorized form f = np.multiply(rowH,colH) A[row][col]=np.sum(np.multiply(f,w)) print(A)
::NOTE:: this code only runs with NumPy 1.7.0 and higher!
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seb about 11 yearsok, my bad :-). should I remove this post or do you think it's useful for others?
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mrjrdnthms about 10 yearsleave it. helped me :)
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Nagabhushan S N over 5 yearsSo, is there any option to multiply multiple arrays in a single call (in latest version) or do we have to chain the calls?
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Shlomi A over 2 yearsThis answer assume that input arrays are 1D. Taking
np.prod(np.array(arrays), axis=0))
works better for multi-dimensional input arrays.