subtract(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])

Some inconsistencies with the Dask version may exist.

Subtract arguments, element-wise.

Notes

Equivalent to x1 - x2 in terms of array broadcasting.

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, 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 :None:None:`out` array will be set to the ufunc result. Elsewhere, the :None:None:`out` array will retain its original value. Note that if an uninitialized :None:None:`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 <ufuncs.kwargs> .

Returns

y : ndarray

The difference of :None:None:`x1` and :None:None:`x2`, element-wise. This is a scalar if both :None:None:`x1` and :None:None:`x2` are scalars.

This docstring was copied from numpy.subtract.

Examples

This example is valid syntax, but we were not able to check execution

>>> np.subtract(1.0, 4.0)  # doctest: +SKIP
-3.0

This example is valid syntax, but we were not able to check execution

>>> x1 = np.arange(9.0).reshape((3, 3))  # doctest: +SKIP
... x2 = np.arange(3.0)  # doctest: +SKIP
... np.subtract(x1, x2)  # doctest: +SKIP
array([[ 0.,  0.,  0.],
       [ 3.,  3.,  3.],
       [ 6.,  6.,  6.]])

The - operator can be used as a shorthand for np.subtract on ndarrays.

This example is valid syntax, but we were not able to check execution

>>> x1 = np.arange(9.0).reshape((3, 3))  # doctest: +SKIP
... x2 = np.arange(3.0)  # doctest: +SKIP
... x1 - x2  # doctest: +SKIP
array([[0., 0., 0.],
       [3., 3., 3.],
       [6., 6., 6.]])

See :

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File: /dask/array/ufunc.py#None
type: <class 'dask.array.ufunc.ufunc'>
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