xarray.Dataset.any#

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

Reduce this Dataset’s data by applying any along some dimension(s).

Parameters:

dim (str, Iterable of Hashable, "..." or None, default: None) – Name of dimension[s] along which to apply any. For e.g. dim="x" or dim=["x", "y"]. If "..." or None, will reduce over all dimensions.
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 any on this object’s data. These could include dask-specific kwargs like split_every.

Returns:

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

Aggregation: User guide on reduction or aggregation operations.

Examples

>>> da = xr.DataArray(
...  np.array([True, True, True, True, True, False], dtype=bool),
...  dims="time",
...  coords=dict(
...  time=("time", pd.date_range("2001年01月01日", freq="ME", periods=6)),
...  labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...  ),
... )
>>> ds = xr.Dataset(dict(da=da))
>>> ds
<xarray.Dataset> Size: 78B
Dimensions: (time: 6)
Coordinates:
 * time (time) datetime64[us] 48B 2001年01月31日 2001年02月28日 ... 2001年06月30日
 labels (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a'
Data variables:
 da (time) bool 6B True True True True True False

>>> ds.any()
<xarray.Dataset> Size: 1B
Dimensions: ()
Data variables:
 da bool 1B True