numpy.sqrt

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

Return the nonnegative squareroot of an array, elementwise.
Parameters: 

x : array_like 
The values whose squareroots are required. 
out : ndarray, None, or tuple of ndarray and None, 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 freshlyallocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. 
where : array_like, optional 
This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default out=None , locations within it where the condition is False will remain uninitialized.  **kwargs

For other keywordonly arguments, see the ufunc docs. 
Returns: 

y : ndarray 
An array of the same shape as x , containing the positive squareroot of each element in x . If any element in x is complex, a complex array is returned (and the squareroots of negative reals are calculated). If all of the elements in x are real, so is y , with negative elements returning nan . If out was provided, y is a reference to it. This is a scalar if x is a scalar. 
See also

lib.scimath.sqrt
 A version which returns complex numbers when given negative reals.
Notes
sqrt has–consistent with common convention–as its branch cut the real “interval” [inf
, 0), and is continuous from above on it. A branch cut is a curve in the complex plane across which a given complex function fails to be continuous.
Examples
>>> np.sqrt([1,4,9])
array([ 1., 2., 3.])
>>> np.sqrt([4, 1, 3+4J])
array([ 2.+0.j, 0.+1.j, 1.+2.j])
>>> np.sqrt([4, 1, np.inf])
array([ 2., nan, inf])