xarray.IndexVariable.mean

xarray.IndexVariable.mean#

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

Reduce this NamedArray’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).

  • **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 (NamedArray) – New NamedArray with mean applied to its data and the indicated dimension(s) removed

See also

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

Aggregation

User guide on reduction or aggregation operations.

Notes

Non-numeric variables will be removed prior to reducing.

Examples

>>> from xarray.namedarray.core import NamedArray
>>> na = NamedArray("x", np.array([1, 2, 3, 0, 2, np.nan]))
>>> na
<xarray.NamedArray (x: 6)> Size: 48B
array([ 1.,  2.,  3.,  0.,  2., nan])

>>> na.mean()
<xarray.NamedArray ()> Size: 8B
array(1.6)

Use skipna to control whether NaNs are ignored.

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