# xarray.ufuncs.sqrt

`xarray.ufuncs.``sqrt` = <xarray.ufuncs._UFuncDispatcher object>

  xarray specific variant of numpy.sqrt. Handles xarray.Dataset, xarray.DataArray, xarray.Variable, numpy.ndarray and dask.array.Array objects with automatic dispatching.

  Documentation from numpy:

  sqrt(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature, extobj])

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

  Parameters:

  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

  > Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone.

  **kwargs

  > For other keyword-only arguments, see the ufunc docs.

  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])