xarray.Dataset.head#

Dataset.head(indexers=None, **indexers_kwargs)[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年01月01日", 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> Size: 120B
Dimensions: (date: 5)
Coordinates:
 * date (date) datetime64[us] 40B 2023年01月05日 2023年01月04日 ... 2023年01月03日
Data variables:
 pageviews (date) int64 40B 2000 1800 1500 1200 900
 visitors (date) int64 40B 1500 1200 1000 800 600

# Retrieve the 3 most busiest days in terms of pageviews

>>> busiest_days.head(3)
<xarray.Dataset> Size: 72B
Dimensions: (date: 3)
Coordinates:
 * date (date) datetime64[us] 24B 2023年01月05日 2023年01月04日 2023年01月02日
Data variables:
 pageviews (date) int64 24B 2000 1800 1500
 visitors (date) int64 24B 1500 1200 1000

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

>>> busiest_days.head({"date": 3})
<xarray.Dataset> Size: 72B
Dimensions: (date: 3)
Coordinates:
 * date (date) datetime64[us] 24B 2023年01月05日 2023年01月04日 2023年01月02日
Data variables:
 pageviews (date) int64 24B 2000 1800 1500
 visitors (date) int64 24B 1500 1200 1000
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