Count#

class ray.data.aggregate.Count(on: str | None = None, ignore_nulls: bool = False, alias_name: str | None = None)[source]#

Bases: VectorizedAggregateFnV2[int, int]

Defines count aggregation.

Examples

>>> import ray
>>> from ray.data.aggregate import Count
>>> ds = ray.data.range(100)
>>> # Schema: {'id': int64}
>>> ds = ds.add_column(
...     "group_key", lambda batch: batch["id"].astype("int64") % 3
... )
>>> # Schema: {'id': int64, 'group_key': int64}
>>> # Counting all rows:
>>> result = ds.aggregate(Count())
>>> # result: {'count()': 100}
>>> # Counting all rows per group:
>>> result = ds.groupby("group_key").aggregate(Count(on="id")).take_all()
>>> # result: [{'group_key': 0, 'count(id)': 34},
>>> #          {'group_key': 1, 'count(id)': 33},
>>> #          {'group_key': 2, 'count(id)': 33}]

Parameters:

on (str | None) – Optional name of the column to count values on. If None, counts rows.
ignore_nulls (bool) – Whether to ignore null values when counting. Only applies if on is specified. Default is False which means Count() on a column will count nulls by default. To match pandas default behavior of not counting nulls, set ignore_nulls=True.
alias_name (str | None) – Optional name for the resulting column.

Methods

`finalize`	Transforms the final accumulated state into the desired output.
`get_agg_name`	Return the agg name (e.g., 'sum', 'mean', 'count').