Introduction
Numpy is a powerful library in Python that offers support for multidimensional arrays and a vast collection of mathematical functions. Among these mathematical functions, the numpy.sqrt() function is very commonly used, as it calculates the square root of every element in the array. In this article, we’ll understand its syntax, usage, and various aspects of the numpy.sqrt() function along with various examples.
Syntax of numpy.sqrt()
The syntax of numpy.sqrt()function is quite simple as explained below.
numpy.sqrt(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature, extobj])
 x: This is the input array, and we want to calculate the square root of every element in this array.
 out: This is an optional parameter. If provided, the function places the result into this array. It should have the same shape and buffer as the expected output.
 Other parameters like where, casting, order, dtype, subok, signature, and extobj are optional and rarely used in standard usage.
Examples of Numpy Square Root Function  numpy.sqrt()
Import Library
We begin with importing the Numpy library as shown below.
In [0]:
import numpy as np
Example 1: Simple Usage of np.sqrt() Function on Scalar
In this example, we simply pass a scalar number to np.sqrt() function and print its square root value.
In [1]:
# Create an array arr = np.array(9) # Calculate the square root of element sqrt_arr = np.sqrt(arr) print(sqrt_arr)
3.0
Example 2: Using np.sqrt() Function on 1D Array
In this example, we first create a 1D array and then pass it to the np.sqrt() function.
In [2]:
# Create an array arr = np.array([1, 4, 9, 16, 25]) # Calculate the square root of each element sqrt_arr = np.sqrt(arr) print(sqrt_arr)
Out[2]:
[1. 2. 3. 4. 5.]
Example 3: Using np.sqrt() Function on 2D Array
In this example, we first create a 2D array and then pass it to the np.sqrt() function.
In [3]:
# Create a 2x3 array arr = np.array([[1, 4, 9], [16, 25, 36]]) # Calculate the square root of each element sqrt_arr = np.sqrt(arr) print(sqrt_arr)
Out[3]:
[[1. 2. 3.] [4. 5. 6.]]
Example 4: Using Complex Numbers in numpy.sqrt()
The numpy.sqrt() function can also be used to calculate the square root of complex numbers as shown in the below example.
In [4]:
# Create a complex number complex_num = np.array( [39j, 54j, 3+5j, 92j]) # Calculate the square root of the complex number sqrt_complex = np.sqrt(complex_num) print(sqrt_complex)
[2.498682951.80094878j 0.837593052.3877944j 2.10130339+1.18973776j 3.018239920.33131892j]
Example 5: Using Infinity in numpy.sqrt()
The numpy.sqrt() function can also be used with infinity. In this example, we pass np.inf as an element inside the array and then we calculate the square root. Since the square root of infinity is infinity only, we get the same as output.
In [5]:
# Create an array arr = np.array([9, np.inf]) # Calculate the square root of each element sqrt_arr = np.sqrt(arr) print(sqrt_arr)
[ 3. inf]
Example 6: Using Negative Numbers in numpy.sqrt()
The square root of a negative number is not possible. Hence when we try to use a negative number inside the array for square root, it returns nan and also prints a warning.
In [6]:
# Create an array arr = np.array([25,9]) # Calculate the square root of each element sqrt_arr = np.sqrt(arr) print(sqrt_arr)
Out[6]:
[ 5. nan]
<ipythoninput5660d5e9dba03>:5: RuntimeWarning: invalid value encountered in sqrt sqrt_arr = np.sqrt(arr)
Example 7: Using Out Parameter of np.sqrt()
To use the out parameter, we first create a zeros array of the same size as the input array and pass it as an out parameter to np.sqrt() function. The output of the square root is then saved inside this out array.
In [7]:
# Create an array arr = np.array([1, 4, 9, 16, 25]) # Create an output array out_arr = np.zeros(5) # Calculate the square root using 'out' parameter np.sqrt(arr, out=out_arr) print(out_arr)
Out[7]:
[1. 2. 3. 4. 5.]
Example 8: Using Where Parameter of np.sqrt()
The ‘where’ parameter is a boolean array with the same shape as the input array. It specifies where the square root function should be applied or not. If the value at a particular index is False, the square root function is not applied, and if its True then it is applied.
In the below example, we create a boolean array of size 3 which is the same as the input array size. The value at the middle index is kept as True and the other two are kept as False. This boolean array is then passed as the ‘where’ parameter. In the output we can see square root is applied only to the middle element because of this ‘where’ parameter.
In [8]:
#Create an array arr = np.array([4, 25, 9]) #Create Out array out = np.array([0.0, 0.0, 0.0]) #Create boolean array where = [False, True, False] #Calculate Square Root sqrt_arr = np.sqrt(arr,out=out,where=where) print(sqrt_arr)
[0. 5. 0.]
Reference: NumPy Documentation

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