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

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

  • 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 None, will reduce over the Resample dimensions. If “…”, 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.


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

See also

numpy.any, dask.array.any, Dataset.any

Resampling and grouped operations

User guide on resampling operations.


>>> da = xr.DataArray(
...     np.array([True, True, True, True, True, False], dtype=bool),
...     dims="time",
...     coords=dict(
...         time=("time", pd.date_range("01-01-2001", freq="M", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> ds = xr.Dataset(dict(da=da))
>>> ds
Dimensions:  (time: 6)
  * time     (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 'a' 'b' 'c' 'c' 'b' 'a'
Data variables:
    da       (time) bool True True True True True False
>>> ds.resample(time="3M").any()
Dimensions:  (time: 3)
  * time     (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31
Data variables:
    da       (time) bool True True True