sqrt(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])
Return the non-negative square-root of an array, element-wise.
x : array_like
The values whose square-roots 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 freshly-allocated 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.
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
For other keyword-only arguments, see the
ufunc docs <ufuncs.kwargs>.
y : ndarray
An array of the same shape as
x, containing the positive
square-root of each element in
x. If any element in
complex, a complex array is returned (and the square-roots of
negative reals are calculated). If all of the elements in
are real, so is
y, with negative elements returning
out was provided,
y is a reference to it.
This is a scalar if
x is a scalar.
emath.sqrt A version which returns complex numbers when given negative reals. Note: 0.0 and -0.0 are handled differently for complex inputs.
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.
>>> 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])