NumPy square() – Square of Input Array Element-wise

NumPy square()

The numpy.square() function computes the element-wise square of the input array, returning the squared values of each element.

Syntax

numpy.square(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True)

Parameters

Parameter	Type	Description
x	array_like	Input data whose elements will be squared.
out	ndarray, None, or tuple of ndarray and None, optional	Optional output array where the result is stored. If None, a new array is created.
where	array_like, optional	Boolean mask specifying which elements should be squared. Elements where where=False retain their original value.
casting	str, optional	Defines the casting behavior when computing the square function.
order	str, optional	Memory layout order of the output array.
dtype	data-type, optional	Defines the data type of the output array.
subok	bool, optional	Determines if subclasses of ndarray are preserved in the output.

Return Value

Returns an array containing the element-wise square of the input values. If the input is a scalar, a scalar is returned.

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Examples

1. Squaring a Single Value

Here, we compute the square of a single number using numpy.square().

import numpy as np

# Define a number
num = 5

# Compute the square
result = np.square(num)

# Print the result
print("Square of", num, ":", result)

Output:

Square of 5 : 25

2. Squaring an Array of Values

We compute the square of multiple elements in an array.

import numpy as np

# Define an array of numbers
arr = np.array([1, 2, 3, 4, 5])

# Compute the square of each element
squared_values = np.square(arr)

# Print the results
print("Original array:", arr)
print("Squared values:", squared_values)

Output:

Original array: [1 2 3 4 5]
Squared values: [ 1  4  9 16 25]

3. Using the out Parameter

Using an output array to store results instead of creating a new array.

import numpy as np

# Define an array of numbers
arr = np.array([2, 4, 6, 8])

# Create an output array with the same shape
output_array = np.empty_like(arr)

# Compute square and store the result in output_array
np.square(arr, out=output_array)

# Print the results
print("Computed squared values:", output_array)

Output:

Computed squared values: [ 4 16 36 64]

4. Using the where Parameter

Using a condition to square only selected elements.

import numpy as np

# Define an array of numbers
arr = np.array([1, 2, 3, 4, 5])

# Define a mask (square only elements greater than 2)
mask = arr > 2

# Compute squared values where mask is True
result = np.square(arr, where=mask)

# Print the results
print("Computed squared values with mask:", result)

Output:

Computed squared values with mask: [1 2 9 16 25]

The square operation is applied only to elements greater than 2. The remaining values remain unchanged.