xarray.Dataset.mean (original) (raw)

Dataset.mean(dim=None, *, skipna=None, keep_attrs=None, **kwargs)[source]#

Reduce this Dataset’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 (Dataset) – New Dataset with mean applied to its data and the indicated dimension(s) removed

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"])), ... ), ... ) ds = xr.Dataset(dict(da=da)) ds <xarray.Dataset> Size: 120B Dimensions: (time: 6) 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' Data variables: da (time) float64 48B 1.0 2.0 3.0 0.0 2.0 nan

ds.mean() <xarray.Dataset> Size: 8B Dimensions: () Data variables: da float64 8B 1.6

Use skipna to control whether NaNs are ignored.

ds.mean(skipna=False) <xarray.Dataset> Size: 8B Dimensions: () Data variables: da float64 8B nan