ray.data.aggregate.ValueCounter#

class ray.data.aggregate.ValueCounter(on: str, alias_name: str | None = None)[source]#

Bases: AggregateFnV2

Counts the number of times each value appears in a column.

This aggregation computes value counts for a specified column, similar to pandas’ value_counts() method. It returns a dictionary with two lists: “values” containing the unique values found in the column, and “counts” containing the corresponding count for each value.

Example

import ray
from ray.data.aggregate import ValueCounter

# Create a dataset with repeated values
ds = ray.data.from_items([
    {"category": "A"}, {"category": "B"}, {"category": "A"},
    {"category": "C"}, {"category": "A"}, {"category": "B"}
])

# Count occurrences of each category
result = ds.aggregate(ValueCounter(on="category"))
# result: {'value_counter(category)': {'values': ['A', 'B', 'C'], 'counts': [3, 2, 1]}}

# Using with groupby
ds = ray.data.from_items([
    {"group": "X", "category": "A"}, {"group": "X", "category": "B"},
    {"group": "Y", "category": "A"}, {"group": "Y", "category": "A"}
])
result = ds.groupby("group").aggregate(ValueCounter(on="category")).take_all()
# result: [{'group': 'X', 'value_counter(category)': {'values': ['A', 'B'], 'counts': [1, 1]}},
#          {'group': 'Y', 'value_counter(category)': {'values': ['A'], 'counts': [2]}}]
Parameters:

  • on – The name of the column to count values in. Must be provided.

  • alias_name – Optional name for the resulting column. If not provided, defaults to “value_counter({column_name})”.

Methods

finalize

Transforms the final accumulated state into the desired output.