DataArrayGroupBy.mean(dim=None, skipna=True, keep_attrs=None, **kwargs)[source]

Reduce this DataArray’s data by applying mean along some dimension(s).

  • dim (hashable or iterable of hashable, optional) – Name of dimension[s] along which to apply mean. For e.g. dim="x" or dim=["x", "y"]. If None, will reduce over all dimensions present in the grouped variable.

  • skipna (bool, 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, optional) – If True, attrs will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.

  • **kwargs (dict) – Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data.


reduced (DataArray) – New DataArray with mean applied to its data and the indicated dimension(s) removed


>>> da = xr.DataArray(
...     np.array([1, 2, 3, 1, 2, np.nan]),
...     dims="time",
...     coords=dict(
...         time=("time", pd.date_range("01-01-2001", freq="M", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> da
<xarray.DataArray (time: 6)>
array([ 1.,  2.,  3.,  1.,  2., nan])
  * time     (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 'a' 'b' 'c' 'c' 'b' 'a'
>>> da.groupby("labels").mean()
<xarray.DataArray (labels: 3)>
array([1., 2., 2.])
  * labels   (labels) object 'a' 'b' 'c'

Use skipna to control whether NaNs are ignored.

>>> da.groupby("labels").mean(skipna=False)
<xarray.DataArray (labels: 3)>
array([nan,  2.,  2.])
  * labels   (labels) object 'a' 'b' 'c'

See also

numpy.mean, DataArray.mean

GroupBy: split-apply-combine

User guide on groupby operations.