numpy.sqrt¶

numpy.sqrt(x[, out]) = <ufunc 'sqrt'>¶

Return the positive square-root of an array, element-wise.

Parameters:

Parameters:	x : array_like The values whose square-roots are required. out : ndarray, optional Alternate array object in which to put the result; if provided, it must have the same shape as x
Returns:	y : ndarray An array of the same shape as x, containing the positive square-root of each element in x. If any element in x is complex, a complex array is returned (and the square-roots 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.

x : array_like

The values whose square-roots are required.

out : ndarray, optional

Alternate array object in which to put the result; if provided, it must have the same shape as x

Returns:

y : ndarray

An array of the same shape as x, containing the positive square-root of each element in x. If any element in x is complex, a complex array is returned (and the square-roots 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.

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, numpy.inf])
array([  2.,  NaN,  Inf])