xarray.core.resample.DatasetResample.any

xarray.core.resample.DatasetResample.any#

DatasetResample.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 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.

Returns

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.

Notes

Use the flox package to significantly speed up resampling computations, especially with dask arrays. Xarray will use flox by default if installed. Pass flox-specific keyword arguments in **kwargs. See the flox documentation for more.

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.resample(time="3ME").any()
<xarray.Dataset> Size: 27B
Dimensions:  (time: 3)
Coordinates:
  * time     (time) datetime64[ns] 24B 2001-01-31 2001-04-30 2001-07-31
Data variables:
    da       (time) bool 3B True True True