xarray.Dataset.tail#

Dataset.tail(indexers=None, **indexers_kwargs)[source] #

Returns a new dataset with the last n values of each array for the specified dimension(s).

Parameters:

indexers (dict or int, 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 of indexers. 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