var
- UnitsAwareDataArray.var(dim: Dims = None, *, skipna: bool | None = None, ddof: int = 0, keep_attrs: bool | None = None, **kwargs: Any) Self
Reduce this DataArray’s data by applying
var
along some dimension(s).- Parameters:
dim (str, Iterable of Hashable, "..." or None, default: None) – Name of dimension[s] along which to apply
var
. For e.g.dim="x"
ordim=["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).ddof (int, default: 0) – “Delta Degrees of Freedom”: the divisor used in the calculation is
N - ddof
, whereN
represents the number of elements.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
var
on this object’s data. These could include dask-specific kwargs likesplit_every
.
- Returns:
reduced – New DataArray with
var
applied to its data and the indicated dimension(s) removed- Return type:
DataArray
See also
numpy.var
,dask.array.var
,Dataset.var
- 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.var() <xarray.DataArray ()> Size: 8B array(1.04)
Use
skipna
to control whether NaNs are ignored.>>> da.var(skipna=False) <xarray.DataArray ()> Size: 8B array(nan)
Specify
ddof=1
for an unbiased estimate.>>> da.var(skipna=True, ddof=1) <xarray.DataArray ()> Size: 8B array(1.3)