numpy.subtract(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'subtract'>
Subtract arguments, element-wise.
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, 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. 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.
For other keyword-only arguments, see the ufunc docs.
y : ndarray
The difference of
x2, element-wise. This is a scalar if both
x2 are scalars.
x1 - x2 in terms of array broadcasting.
>>> np.subtract(1.0, 4.0)
>>> 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.]])