NumPy sqrt() - Square Root of Matrix Elements

import numpy
array_2d = numpy.array([[1, 4], [9, 16]], dtype=numpy.float)
print(array_2d)
array_2d_sqrt = numpy.sqrt(array_2d)
print(array_2d_sqrt)

Output:

[[ 1.  4.]
 [ 9. 16.]]
[[1. 2.]
 [3. 4.]]

Let’s look at another example where the matrix elements are not square of integers. This time we will use the Python interpreter.

>>> import numpy
>>> 
>>> array = numpy.array([[1, 3], [5, 7]], dtype=numpy.float)
>>> 
>>> print(array)
[[1. 3.]
 [5. 7.]]
>>> 
>>> array_sqrt = numpy.sqrt(array)
>>> 
>>> print(array_sqrt)
[[1.         1.73205081]
 [2.23606798 2.64575131]]
>>> 

Let’s see what happens when we have infinity as the matrix element.

>>> array = numpy.array([1, numpy.inf])
>>> 
>>> numpy.sqrt(array)
array([ 1., inf])
>>> 

>>> array = numpy.array([1 + 2j, -3 + 4j], dtype=numpy.complex)
>>> 
>>> numpy.sqrt(array)
array([1.27201965+0.78615138j, 1.        +2.j        ])
>>> 

>>> array = numpy.array([4, -4])
>>> 
>>> numpy.sqrt(array)
__main__:1: RuntimeWarning: invalid value encountered in sqrt
array([ 2., nan])
>>> 

The square root of a matrix with negative numbers will throw RuntimeWarning and the square root of the element is returned as nan. Reference: NumPy Docs