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• Post category:NumPy / Python

In Python, NumPy is a powerful library for numerical computing. You can use NumPy to calculate the square root of elements in an array using the `numpy.sqrt()` function. This function is used to return the non-negative square root of an array element-wise (for each element of the array). In this article, I will explain how to use Numpy square root by using `numpy.sqrt()` function with examples.

## 1. Quick Examples of Square Root

If you are in a hurry, below are some quick examples of how to use the Python NumPy square root.

``````
# Quick examples of square root

# Example 1: Use numpy.sqrt() function
# Get single-element
arr = np.array(25)
arr2 = np.sqrt(arr)

# Example 2: Use numpy.sqrt() function
# Get multiple square root values
arr =np.array([25, 49, 225, 64, 81, 16])
arr2 = np.sqrt(arr)

# Example 3: Calculate square roots of complex numbers
arr = [2+6j, -5-8j, 4-5j, 3+4j]
arr2 = np.sqrt(arr)

# Example 4: Use numpy.sqrt() function
# With negative and inifite as input values
arr = [-6, np.inf, 25, -15, np.inf]
arr2 = np.sqrt(arr)

# Example 5: Use numpy.sqrt() function
# To floating-point array
arr = [4.3, 8.5, 15.1, 23.7, 14.2, 7.8]
arr2 = np.sqrt(arr)

# Example 6: Use numpy.sqrt() function
# Get the square root values of 2-d array
arr = np.array([[25, 64, 9, 16], [9, 4, 49, 36]])
arr2 = np.sqrt(arr)
``````

## 2. Syntax of sqrt()

Following is the syntax of the `numpy.sqrt()` function.

``````
# Syntax of python numpy.sqrt()
numpy.sqrt(arr, out=None, where=True, casting='same_kind', order='K', dtype=None)
``````

### 2.1 Parameters of sqrt()

• `arr` – The values whose square roots are required. Input array.
• `out` – It is ndarray, None, or tuple of ndarray and None, optional. Out will be the location where the result is to be 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` – It is array_like, optional. This condition is broadcast over the input.

### 2.2 Return Value

It returns an array of the square root of the number in the input array.

## 3. Usage of NumPy Square Root Function

You can get the square root of the single element of an array using `numpy.sqrt()` function. You can also get the square values of the NumPy array using `numpy.square()`.

``````
import numpy as np

# Create a single element
arr = np.array(25)
print("Original array:", arr)

# Use numpy.sqrt() function
# Get single element
arr2 = np.sqrt(arr)
print("After getting the square root value:",arr2)
``````

Yields below output.

## 4. Get the Multiple Square Root Values

If you want to calculate square roots for multiple values, you can pass a list, tuple, or another array-like object to the `numpy.sqrt()` function. To initialize the array with list of numbers use `numpy.array()` and calculate the square root of these numbers by using the `numpy.sqrt()` function.

In the below example, `numpy.sqrt()` calculates the square root of each number in the `arr` list, resulting in `[ 5. 7. 15. 8. 9. 4.]`.

``````
import numpy as np

# Create an input array
arr =np.array([25, 49, 225, 64, 81, 16])
print("Original array:\n",arr)

# Use numpy.sqrt() function
# Get multiple square root values
arr2 = np.sqrt(arr)
print("After getting the multiple square root values:\n",arr2)
``````

Yields below output.

## 5. Get the Square Roots of Complex Numbers

NumPy can handle complex numbers and can calculate square roots of complex numbers using the `numpy.sqrt()` function. Complex numbers in NumPy are represented using the `numpy.complex` data type.

In the below example, `numpy.sqrt()` calculates the square roots of the complex numbers `[2+6j, -5-8j, 4-5j, 3+4j]`. The result is an array of complex numbers representing the square roots of the input complex numbers.

``````
import numpy as np

# Define complex numbers
arr = np.array([2+6j, -5-8j, 4-5j, 3+4j])
print("Original complex numbers:\n", arr)

# Calculate square roots of complex numbers
arr2 = np.sqrt(arr)
print("After getting the square roots of complex numbers:\n",arr2)

# Output:
# Original complex numbers:
#  [ 2.+6.j -5.-8.j  4.-5.j  3.+4.j]
# After getting the square roots of complex numbers:
#  [2.04016609+1.47046852j 1.4889562 -2.68644571j 2.28069334-1.09615789j
#  2.        +1.j        ]
``````

## 6. Get The Square Roots of Negative and Infinite Values

Using the` numpy.sqrt()` function you can also calculate the square root of the negative and Infinite as input values of an array. The square root of a matrix with negative numbers will throw RuntimeWarning and the square root of the element is returned as `nan` as a result.

``````
import numpy as np

# Create input array
arr =np.array[-6, np.inf, 25, -15, np.inf]

# Use numpy.sqrt() function
# With negative and infinite
arr2 = np.sqrt(arr)
print("After getting negative and infinite values:\n",arr2)

# Output:
# [nan inf  5. nan inf]
# RuntimeWarning: invalid value encountered in sqrt
``````

## 7. Get the Square root of NumPy Array with Float Values

You can calculate the square root of a NumPy array containing float values using the `numpy.sqrt()` function. For instance, `numpy.sqrt()` calculates the square root of each element in the `arr`.

``````
import numpy as np

# Create an 1D input array
arr = np.array( [4.3, 8.5, 15.1, 23.7, 14.2, 7.8])

# Use numpy.sqrt() function
# To floating-point array
arr2 = np.sqrt(arr)
print("After getting the square roots of float values:\n",arr2)

# Output:
# After getting the square roots of float values:
#  [2.07364414 2.91547595 3.88587185 4.86826458 3.76828874 2.79284801]
``````

## 8. Get the Square Roots of 2-D NumPy Array Values

You can calculate the square roots of a 2-D NumPy array’s values using the `numpy.sqrt()` function. For instance, `numpy.sqrt()` calculates the square roots of each element in the 2-D array, resulting in a new 2-D array where each element is the square root of the corresponding element in the original array. The function performs element-wise square root calculations efficiently on the entire 2-D array.

``````
import numpy as np

# Create an 2D input array
arr = np.array([[25, 64, 9, 16], [9, 4, 49, 36]])

# Use numpy.sqrt() function
# Get the square root values of 2-d array
arr2 = np.sqrt(arr)
print("After getting the square root 2-D array:\n",arr2)

# Output:
# After getting the square root 2-D array:
#  [[5. 8. 3. 4.]
#  [3. 2. 7. 6.]]
``````

What is the purpose of numpy.sqrt() function?

The `numpy.sqrt()` function in NumPy serves the purpose of computing the square root of each element in a NumPy array. It performs element-wise square root calculations, which means it takes an input array and returns a new array where each element is replaced with its square root.

How can I use numpy.sqrt() to calculate square roots?

You can use `numpy.sqrt()` by passing a NumPy array as an argument. It will return a new array containing the square root of each element in the input array.

Can I calculate square roots for multi-dimensional arrays?

Using the `numpy.sqrt()` function can be applied to multi-dimensional arrays, including 2-D arrays. It performs element-wise operations on each element in the array.

How do I handle complex numbers with numpy.sqrt()?

NumPy’s `numpy.sqrt()` function can handle complex numbers naturally. When you apply `numpy.sqrt()` to an array that contains complex numbers, it performs the square root operation element-wise, preserving the complex nature of the numbers.

Can numpy.sqrt() handle NaN (Not a Number) values?

`numpy.sqrt()` can handle NaN (Not a Number) values. When you apply the `numpy.sqrt()` function to an array that contains NaN values, it calculates the square root element-wise, and if a NaN value is encountered, the result will be NaN as well.

How does numpy.sqrt() handle negative numbers?

You use `numpy.sqrt()` to calculate the square root of negative numbers, it returns complex numbers. In the context of complex numbers, the square root of a negative number is a complex number with a real part of 0 and an imaginary part corresponding to the positive square root of the absolute value of the input.

## Conclusion

In this article, I have explained how to use Python `numpy.sqrt()` function to calculate the square root of every element in the given array with examples.

Happy Learning!!