.square()

In NumPy, the .square() method computes the square of a number or the square of the elements in an array. It is commonly used in mathematical calculations, machine learning, data analysis, engineering, and graphics.

Syntax

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

• x: The input data, which can be a number, an array, or a multidimensional array.
• out (Optional): A location where the result is stored. If provided, it must have the same shape as the expected output.
• where (Optional): A boolean array specifying which elements to compute. The result is only computed for elements where where is True.
• dtype (Optional): The desired data type for the output array. If not specified, it defaults to the data type of x.

Examples

Modifying the output array

The output array for NumPy operations cannot be a Python list because lists are not optimized for numerical computations. NumPy arrays are composed of contiguous blocks of memory, which enhances performance. Therefore, the array passed for the out parameter must be a NumPy array initialized with the numpy.array function:

import numpy as np

output_array = np.array([0, 0, 0, 0, 0])

This array can then be used as the out parameter in the numpy.square() function:

import numpy as np

output_array = np.array([0, 0, 0, 0, 0])

array = [1, 2, 3, 4, 5]
np.square(array, out = output_array)
print(output_array)

This generates the output as follows:

[1, 4, 9, 16, 25]

Operating conditionally

Using the where parameter, the function will execute conditionally. The where parameter specifies where to apply the operation, based on a condition. If the condition is True at a particular index, the corresponding element in the array will be squared. If the condition is False, the element will remain unchanged. For instance:

import numpy as np

array = np.array([1, 2, 3, 4, 5])
conditions = np.array([False, True, True, False, True])

result = np.square(array, where=conditions)
print(result)

Output:

array([1, 4, 9, 4, 25])

The where parameter takes a boolean array or condition. It determines where the squaring operation will take place:

• True at an index: The element at that index will be squared.
• False at an index: The element at that index will remain unchanged.

If the where parameter is set to a single boolean value (either True or False), the entire array is either squared (if True) or left unchanged (if False).

Changing types

Sometimes, it is important to increase or decrease the size of the datatype of the output array. This can be done by setting the dtype parameter to an np datatype, like:

import numpy as np
array = np.array([1, 2, 3, 4, 5])  # Ensuring it's a numpy array
result = np.square(array, dtype=np.float32)

# Print the result
print(result)

Output generated will be as follows:

array([ 1.,  4.,  9., 16., 25.], dtype=float32)

Codebyte Example

Run the following example to understand how the .square() method works:

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