xarray.DataArray.max#

DataArray.max(dim=None, *, skipna=None, keep_attrs=None, **kwargs)[source] #

Reduce this DataArray’s data by applying max along some dimension(s).

Parameters:

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

Returns:

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

Aggregation: User guide on reduction or aggregation operations.

Examples

>>> da = xr.DataArray(
...  np.array([1, 2, 3, 0, 2, np.nan]),
...  dims="time",
...  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"])),
...  ),
... )
>>> da
<xarray.DataArray (time: 6)> Size: 48B
array([ 1., 2., 3., 0., 2., nan])
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'

>>> da.max()
<xarray.DataArray ()> Size: 8B
array(3.)

Use skipna to control whether NaNs are ignored.

>>> da.max(skipna=False)
<xarray.DataArray ()> Size: 8B
array(nan)