# xarray.computation.rolling.DatasetRolling.reduce

#### DatasetRolling.reduce(func, keep_attrs=None, sliding_window_view_kwargs=None, \*\*kwargs)[[source]](https://github.com/pydata/xarray/blob/main/xarray/computation/rolling.py#L950-L1000)

Reduce the items in this group by applying func along some
dimension(s).

* **Parameters:**
  * **func** ([`callable()`](https://docs.python.org/3/builtins/functions.html#callable)) – Function which can be called in the form
    func(x, \*\*kwargs) to return the result of collapsing an
    np.ndarray over the rolling dimension.
  * **keep_attrs** ([`bool`](https://docs.python.org/3/builtins/functions.html#bool), *default*: [`None`](https://docs.python.org/3/builtins/constants.html#None)) – If True, the attributes (`attrs`) will be copied from the original
    object to the new one. If False, the new object will be returned
    without attributes. If None uses the global default.
  * **sliding_window_view_kwargs** (`Mapping`) – Keyword arguments that should be passed to the underlying array type’s
    `sliding_window_view` function.
  * **\*\*kwargs** ([`dict`](https://docs.python.org/3/builtins/stdtypes.html#dict)) – Additional keyword arguments passed on to func.
* **Returns:**
  **reduced** ([`DataArray`](xarray.DataArray.html.md#xarray.DataArray)) – Array with summarized data.

#### SEE ALSO
[`numpy.lib.stride_tricks.sliding_window_view`](https://numpy.org/doc/stable/reference/generated/numpy.lib.stride_tricks.sliding_window_view.html#numpy.lib.stride_tricks.sliding_window_view), [`dask.array.lib.stride_tricks.sliding_window_view`](https://docs.dask.org/en/latest/generated/dask.array.lib.stride_tricks.sliding_window_view.html#dask.array.lib.stride_tricks.sliding_window_view)

### Notes

With dask arrays, it’s possible to pass the `automatic_rechunk` kwarg as
`sliding_window_view_kwargs={"automatic_rechunk": True}`. This controls
whether dask should automatically rechunk the output to avoid
exploding chunk sizes. Automatically rechunking is the default behaviour.
Importantly, each chunk will be a view of the data so large chunk sizes are
only safe if *no* copies are made later.