Reduce this Dataset’s data by applying min along some dimension(s).
dim (str, Iterable of Hashable, "..." or None, default: None) – Name of dimension[s] along which to apply min. For e.g. dim="x" or dim=["x", "y"]. If “…” or None, will reduce over all dimensions.
skipna (bool or None, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
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 min on this object’s data. These could include dask-specific kwargs like split_every.
reduced (Dataset) – New Dataset with min applied to its data and the indicated dimension(s) removed
Examples
>>> da = xr.DataArray(
... np.array([1, 2, 3, 0, 2, np.nan]),
... 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: 120B
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) float64 48B 1.0 2.0 3.0 0.0 2.0 nan
>>> ds.min()
<xarray.Dataset> Size: 8B
Dimensions: ()
Data variables:
da float64 8B 0.0
Use skipna to control whether NaNs are ignored.
>>> ds.min(skipna=False)
<xarray.Dataset> Size: 8B
Dimensions: ()
Data variables:
da float64 8B nan
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