# xarray.DataTree.mean
#### DataTree.mean(dim=None, , skipna=None, keep_attrs=None, \*\*kwargs)[[source]](https://github.com/pydata/xarray/blob/main/xarray/core/_aggregations.py#L475-L572)
Reduce this DataTree’s data by applying `mean` along some dimension(s).
* **Parameters:**
* **dim** ([`str`](https://docs.python.org/3/builtins/stdtypes.html#str), `Iterable` of `Hashable`, `"..."` or [`None`](https://docs.python.org/3/builtins/constants.html#None), *default*: [`None`](https://docs.python.org/3/builtins/constants.html#None)) – Name of dimension[s] along which to apply `mean`. For e.g. `dim="x"`
or `dim=["x", "y"]`. If “…” or None, will reduce over all dimensions.
* **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, `attrs` will be copied from the original
object to the new one. If False, the new object will be
returned without attributes.
* **\*\*kwargs** (`Any`) – Additional keyword arguments passed on to the appropriate array
function for calculating `mean` on this object’s data.
These could include dask-specific kwargs like `split_every`.
* **Returns:**
**reduced** ([`DataTree`](xarray.DataTree.html.md#xarray.DataTree)) – New DataTree with `mean` applied to its data and the
indicated dimension(s) removed
#### SEE ALSO
[`numpy.mean`](https://numpy.org/doc/stable/reference/generated/numpy.mean.html#numpy.mean), [`dask.array.mean`](https://docs.dask.org/en/latest/generated/dask.array.mean.html#dask.array.mean), [`Dataset.mean`](xarray.Dataset.mean.html.md#xarray.Dataset.mean), [`DataArray.mean`](xarray.DataArray.mean.html.md#xarray.DataArray.mean)
[Aggregation](../user-guide/computation.html.md#agg)
: User guide on reduction or aggregation operations.
### Notes
Non-numeric variables will be removed prior to reducing. datetime64 and timedelta64 dtypes are treated as numeric for aggregation operations.
### Examples
```pycon
>>> dt = xr.DataTree(
... xr.Dataset(
... data_vars=dict(foo=("time", np.array([1, 2, 3, 0, 2, np.nan]))),
... coords=dict(
... time=(
... "time",
... pd.date_range("2001-01-01", freq="ME", periods=6),
... ),
... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
... ),
... ),
... )
>>> dt
Group: /
Dimensions: (time: 6)
Coordinates:
* time (time) datetime64[us] 48B 2001-01-31 2001-02-28 ... 2001-06-30
labels (time) >> dt.mean()
Group: /
Dimensions: ()
Data variables:
foo float64 8B 1.6
```
Use `skipna` to control whether NaNs are ignored.
```pycon
>>> dt.mean(skipna=False)
Group: /
Dimensions: ()
Data variables:
foo float64 8B nan
```