xarray.Dataset.pr.any

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xarray.Dataset.pr.any#

Dataset.pr.any(dim: str | Collection[Hashable] | ellipsis | None = None, *, keep_attrs: bool | None = None, **kwargs: Any) → Self#

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

Parameters:
dimstr, 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_attrsbool 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.

**kwargsAny

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:
reducedDataset

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

See also

numpy.any
dask.array.any
DataArray.any
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("2001-01-01", freq="ME", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> ds = xr.Dataset(dict(da=da))
>>> ds
<xarray.Dataset> Size: 78B
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) bool 6B True True True True True False

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