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Python numpy.square() function returns a new array with the element value as the square of the source array elements. The source array remains unchanged.
It’s a utility function to quickly get the square of the matrix elements. Let’s look at the examples of numpy square() function with integer, float, and complex type array elements.
import numpy as np
# ints
array_2d = np.array([[1, 2, 3], [4, 5, 6]])
print(f'Source Array:\n{array_2d}')
array_2d_square = np.square(array_2d)
print(f'Squared Array:\n{array_2d_square}')
Output:
Source Array:
[[1 2 3]
[4 5 6]]
Squared Array:
[[ 1 4 9]
[16 25 36]]
import numpy as np
array_2d_float = np.array([1.2, 2.3, 5])
print(f'Source Array:\n{array_2d_float}')
array_2d_float_square = np.square(array_2d_float)
print(f'Squared Array:\n{array_2d_float_square}')
Output:
Source Array:
[1.2 2.3 5. ]
Squared Array:
[ 1.44 5.29 25. ]
Notice that the integer in the floating-point array has been converted to a floating-point number.
arr = np.array([1 + 2j, 2 + 3j, 4])
print(f'Source Array:\n{arr}')
arr_square = np.square(arr)
print(f'Squared Array:\n{arr_square}')
Output:
Source Array:
[1.+2.j 2.+3.j 4.+0.j]
Squared Array:
[-3. +4.j -5.+12.j 16. +0.j]
Here the integer element is converted to a complex number. Reference: API Doc
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