# xarray.computation.weighted.DatasetWeighted.mean

#### DatasetWeighted.mean(dim=None, , skipna=None, keep_attrs=None)[[source]](https://github.com/pydata/xarray/blob/main/xarray/computation/weighted.py#L485-L494)

Reduce this Dataset’s data by a weighted `mean` along some dimension(s).

* **Parameters:**
  * **dim** (`Hashable` or `Iterable` of `Hashable`, *optional*) – Dimension(s) over which to apply the weighted `mean`.
  * **skipna** ([`bool`](https://docs.python.org/3/builtins/functions.html#bool) or [`None`](https://docs.python.org/3/builtins/constants.html#None), *optional*) – If True, skip missing values (as marked by NaN). By default, only
    skips missing values for float dtypes; other dtypes either do not
    have a sentinel missing value (int) or skipna=True has not been
    implemented (object, datetime64 or timedelta64).
  * **keep_attrs** ([`bool`](https://docs.python.org/3/builtins/functions.html#bool) or [`None`](https://docs.python.org/3/builtins/constants.html#None), *optional*) – If True, the attributes (`attrs`) will be copied from the original
    object to the new one.  If False (default), the new object will be
    returned without attributes.
* **Returns:**
  **reduced** ([`Dataset`](xarray.Dataset.html.md#xarray.Dataset)) – New Dataset object with weighted `mean` applied to its data and
  the indicated dimension(s) removed.

### Notes

Returns NaN if the `weights` sum to 0.0 along the reduced
dimension(s).