xarray.DataTree.all#

DataTree.all(dim=None, *, keep_attrs=None, **kwargs)[source] #

Reduce this DataTree’s data by applying all along some dimension(s).

Parameters:

dim (str, Iterable of Hashable, "..." or None, default: None) – Name of dimension[s] along which to apply all. 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 all on this object’s data. These could include dask-specific kwargs like split_every.

Returns:

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

Aggregation: User guide on reduction or aggregation operations.

Examples

>>> dt = xr.DataTree(
...  xr.Dataset(
...  data_vars=dict(
...  foo=(
...  "time",
...  np.array([True, True, True, True, True, False], dtype=bool),
...  )
...  ),
...  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"])),
...  ),
...  ),
... )
>>> dt
<xarray.DataTree>
Group: /
 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:
 foo (time) bool 6B True True True True True False

>>> dt.all()
<xarray.DataTree>
Group: /
 Dimensions: ()
 Data variables:
 foo bool 1B False