mean

UnitsAwareDataArray.mean(*args, **kwargs)[source]

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

Parameters:

dim (str, Iterable of Hashable, "..." or None, default: 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 or 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 or 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 – New DataArray with mean applied to its data and the indicated dimension(s) removed

Return type:

DataArray

See also

numpy.mean, dask.array.mean, Dataset.mean

Aggregation: User guide on reduction or aggregation operations.

Notes

Non-numeric variables will be removed prior to reducing.

Examples

>>> da = xr.DataArray(
...     np.array([1, 2, 3, 0, 2, np.nan]),
...     dims="time",
...     coords=dict(
...         time=("time", pd.date_range("2001-01-01", freq="ME", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> da
<xarray.DataArray (time: 6)> Size: 48B
array([ 1.,  2.,  3.,  0.,  2., nan])
Coordinates:
  * time     (time) datetime64[ns] 48B 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a'

>>> da.mean()
<xarray.DataArray ()> Size: 8B
array(1.6)

Use skipna to control whether NaNs are ignored.

>>> da.mean(skipna=False)
<xarray.DataArray ()> Size: 8B
array(nan)