Attention
This repository has been archived. Please use xarray.DataTree instead.
datatree.DataTree.tail#
- DataTree.tail(indexers: Mapping[Any, int] | int | None = None, **indexers_kwargs: Any) Self [source]#
Returns a new dataset with the last n values of each array for the specified dimension(s).
- Parameters:
indexers (
dict
orint
, default:5
) – A dict with keys matching dimensions and integer values n or a single integer n applied over all dimensions. One of indexers or indexers_kwargs must be provided.**indexers_kwargs (
{dim: n, ...}
, optional) – The keyword arguments form ofindexers
. One of indexers or indexers_kwargs must be provided.
Examples
>>> activity_names = ["Walking", "Running", "Cycling", "Swimming", "Yoga"] >>> durations = [30, 45, 60, 45, 60] # in minutes >>> energies = [150, 300, 250, 400, 100] # in calories >>> dataset = xr.Dataset( ... { ... "duration": (["activity"], durations), ... "energy_expenditure": (["activity"], energies), ... }, ... coords={"activity": activity_names}, ... ) >>> sorted_dataset = dataset.sortby("energy_expenditure", ascending=False) >>> sorted_dataset <xarray.Dataset> Size: 240B Dimensions: (activity: 5) Coordinates: * activity (activity) <U8 160B 'Swimming' 'Running' ... 'Yoga' Data variables: duration (activity) int64 40B 45 45 60 30 60 energy_expenditure (activity) int64 40B 400 300 250 150 100
# Activities with the least energy expenditures using tail()
>>> sorted_dataset.tail(3) <xarray.Dataset> Size: 144B Dimensions: (activity: 3) Coordinates: * activity (activity) <U8 96B 'Cycling' 'Walking' 'Yoga' Data variables: duration (activity) int64 24B 60 30 60 energy_expenditure (activity) int64 24B 250 150 100
>>> sorted_dataset.tail({"activity": 3}) <xarray.Dataset> Size: 144B Dimensions: (activity: 3) Coordinates: * activity (activity) <U8 96B 'Cycling' 'Walking' 'Yoga' Data variables: duration (activity) int64 24B 60 30 60 energy_expenditure (activity) int64 24B 250 150 100
See also
Dataset.head
,Dataset.thin
,DataArray.tail