NumPy square()

import numpy as np

array1 = np.array([1, 2, 3, 4])

# compute the square of array1 elements
result = np.square(array1)

print(result)  

# Output: [ 1  4  9 16]

square() Syntax

The syntax of square() is:

numpy.square(array, out = None, where = True, dtype = None)

square() Arguments

The square() function takes following arguments:

• array1 - the input array
• out (optional) - the output array where the result will be stored
• where (optional) - used for conditional replacement of elements in the output array
• dtype (optional) - data type of the output array

square() Return Value

The square() function returns the array containing the element-wise squares of the input array.

Example 1: Use of dtype Argument in square()

import numpy as np

# create an array
array1 = np.array([1, 2, 3, 4])

# compute the square of array1 with different data types
result_float = np.square(array1, dtype=np.float32)
result_int = np.square(array1, dtype=np.int64)

# print the resulting arrays 
print("Result with dtype=np.float32:", result_float)
print("Result with dtype=np.int64:", result_int)

Output

Result with dtype=np.float32: [ 1.  4.  9. 16.]
Result with dtype=np.int64: [ 1  4  9 16]

Example 2: Use of out and where in square()

import numpy as np

# create an array
array1 = np.array([-2, -1, 0, 1, 2])

# create an empty array of same shape of array1 to store the result
result = np.zeros_like(array1)

# compute the square of array1 where the values are positive and store the result in result array
np.square(array1, where=array1 > 0, out=result)

print("Result:", result)

Output

Result: [0 0 0 1 4]

Here,

• The where argument specifies a condition, array1 > 0, which checks if each element in array1 is greater than zero .
• The out argument is set to result which specifies that the result will be stored in the result array.

For any element in array1 that is not greater than 0 will result in 0.