NumPy sqrt() Function

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The NumPy sqrt() function is used to compute the square root of all elements in an input array. It calculates √x for each element x in the array.

This function can be applied to scalars, lists, or NumPy arrays and will return an array of the same shape with the square root of each input value. If a negative number is provided, it will return NaN (Not a Number) unless complex numbers are supported.

Syntax

Following is the syntax of the NumPy sqrt() function −

numpy.sqrt(x, /, out=None, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])

Parameters

This function accepts the following parameters −

• x: The input array or scalar. The function computes the square root of each element of the array or scalar.
• out (optional): A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned.
• where (optional): This condition is broadcast over the input. At locations where the condition is True, the result will be computed. Otherwise, the result will retain its original value.
• casting (optional): Controls what kind of data casting may occur. Defaults to 'same_kind'.
• order (optional): Controls the memory layout order of the result. 'C' means C-order, 'F' means Fortran-order, 'A' means 'F' if inputs are all F, 'C' otherwise, 'K' means match the layout of the inputs as closely as possible.
• dtype (optional): The type of the returned array and of the accumulator in which the elements are processed. The dtype of x is used by default unless dtype is specified.
• subok (optional): If True, then sub-classes will be passed-through, otherwise the returned array will be forced to be a base-class array.

Return Value

This function returns an array where each element is the square root of the corresponding element in the input array x. If out is provided, it returns a reference to out.

Example: Basic Usage of sqrt() Function

In the following example, we use the sqrt() function to compute the square root for each element in a 1-dimensional array −

import numpy as np

# Creating a 1-dimensional array
arr = np.array([1, 4, 9, 16])

# Applying sqrt to each element
result = np.sqrt(arr)
print(result)

The output obtained will be −

[1. 2. 3. 4.]

Example: sqrt() Function with Broadcasting

In this example, we demonstrate the use of broadcasting with the sqrt() function. We create a 2-dimensional array and apply sqrt on it −

import numpy as np

# Creating a 2-dimensional array
arr = np.array([[1, 4, 9], [16, 25, 36]])

# Applying sqrt to each element
result = np.sqrt(arr)
print(result)

This will produce the following result −

[[1. 2. 3.]
 [4. 5. 6.]]

Example: sqrt() Function with Scalar

In this example, we apply the sqrt() function to a scalar value −

import numpy as np

# Scalar value
scalar = 25

# Applying sqrt to the scalar
result = np.sqrt(scalar)
print(result)

The output obtained is −

5.0

Example: sqrt() Function with Negative Values (Warning)

In this example, we apply the sqrt() function to an array with negative values. Since the square root of a negative number is undefined in the real number system, it will return NaN unless complex numbers are supported −

import numpy as np

# Creating a 1-dimensional array with negative values
arr = np.array([-1, -4, -9])

# Applying sqrt to each element (will raise a warning for negative values)
result = np.sqrt(arr)
print(result)

This will produce the following warning −

/home/cg/root/673acdd6238d1/main.py:7: RuntimeWarning: invalid value encountered in sqrt
  result = np.sqrt(arr)
[nan nan nan]