datatree.DataTree.head#

DataTree.head(indexers: collections.abc.Mapping[Any, int] | int | None = None, **indexers_kwargs: Any) → Self[source]#

Returns a new dataset with the first 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

>>> dates = pd.date_range(start="2023-01-01", periods=5)
>>> pageviews = [1200, 1500, 900, 1800, 2000]
>>> visitors = [800, 1000, 600, 1200, 1500]
>>> dataset = xr.Dataset(
...     {
...         "pageviews": (("date"), pageviews),
...         "visitors": (("date"), visitors),
...     },
...     coords={"date": dates},
... )
>>> busiest_days = dataset.sortby("pageviews", ascending=False)
>>> busiest_days.head()
<xarray.Dataset>
Dimensions:    (date: 5)
Coordinates:
  * date       (date) datetime64[ns] 2023-01-05 2023-01-04 ... 2023-01-03
Data variables:
    pageviews  (date) int64 2000 1800 1500 1200 900
    visitors   (date) int64 1500 1200 1000 800 600

# Retrieve the 3 most busiest days in terms of pageviews

>>> busiest_days.head(3)
<xarray.Dataset>
Dimensions:    (date: 3)
Coordinates:
  * date       (date) datetime64[ns] 2023-01-05 2023-01-04 2023-01-02
Data variables:
    pageviews  (date) int64 2000 1800 1500
    visitors   (date) int64 1500 1200 1000

# Using a dictionary to specify the number of elements for specific dimensions

>>> busiest_days.head({"date": 3})
<xarray.Dataset>
Dimensions:    (date: 3)
Coordinates:
  * date       (date) datetime64[ns] 2023-01-05 2023-01-04 2023-01-02
Data variables:
    pageviews  (date) int64 2000 1800 1500
    visitors   (date) int64 1500 1200 1000