xarray.ufuncs.subtract#
- xarray.ufuncs.subtract = <xarray.ufuncs._binary_ufunc object>#
xarray specific variant of
numpy.subtract()
. Handles xarray objects by dispatching to the appropriate function for the underlying array type.Documentation from numpy:
Subtract arguments, element-wise.
- Parameters
x1, x2 (array_like) – The arrays to be subtracted from each other. If
x1.shape != x2.shape
, they must be broadcastable to a common shape (which becomes the shape of the output).out (
ndarray
,None
, ortuple
ofndarray
andNone
, 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. 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 keyword-only arguments, see the ufunc docs.
- Returns
y (
ndarray
) – The difference of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars.
Notes
Equivalent to
x1 - x2
in terms of array broadcasting.Examples
>>> np.subtract(1.0, 4.0) -3.0
>>> x1 = np.arange(9.0).reshape((3, 3)) >>> x2 = np.arange(3.0) >>> np.subtract(x1, x2) array([[ 0., 0., 0.], [ 3., 3., 3.], [ 6., 6., 6.]])
The
-
operator can be used as a shorthand fornp.subtract
on ndarrays.>>> x1 = np.arange(9.0).reshape((3, 3)) >>> x2 = np.arange(3.0) >>> x1 - x2 array([[0., 0., 0.], [3., 3., 3.], [6., 6., 6.]])