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Python NumPy module is used to work with multidimensional arrays and matrix manipulations. We can use NumPy sqrt() function to get the square root of the 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)
[[ 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
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