Elementwise Math

Elementwise Product

Two 6-element lists multiplied position by position in an index loop. The replay shows i stepping through each index and result accumulating one pairwise product per iteration.

By hand

With NumPy

arr_a * arr_b multiplies the two arrays elementwise without an explicit loop, producing the Hadamard (elementwise) product.

naive.py 
a = [1, 2, 3, 4, 5, 6]
b = [2, 3, 4, 5, 6, 7]
result = []
for i in range(len(a)):
    result.append(a[i] * b[i])
print('RESULT:', result)
library.py 
import numpy as np

a = [1, 2, 3, 4, 5, 6]
b = [2, 3, 4, 5, 6, 7]
arr_a = np.array(a)
arr_b = np.array(b)
result = arr_a * arr_b
print('shape:', result.shape)
print('dtype:', result.dtype)
print('values:', result.tolist())
print('RESULT:', result.tolist())

shape: (6,)
dtype: int64
values: [2, 6, 12, 20, 30, 42]
RESULT: [2, 6, 12, 20, 30, 42]

Implementation notes

  • This is the Hadamard product: position i of the output is a[i] * b[i]. It is NOT the dot product (sum of pairwise products). For dot product use np.dot(a, b) or a @ b — that scalar output lesson is in the roadmap linear-algebra chapter.
  • Both arrays must have compatible shapes. The resulting array has the same shape as the inputs; no reduction occurs.
  • Shape, dtype, and values are shown explicitly here because ndarray.__repr__ output varies with NumPy version and print options.