MissingValuePercentage

class ray.data.aggregate.MissingValuePercentage(on: str, alias_name: str | None = None)

Bases: AggregateFnV2[List[int], float]

Calculates the percentage of null values in a column.

This aggregation computes the percentage of null (missing) values in a dataset column. It treats both None values and NaN values as null. The result is a percentage value between 0.0 and 100.0, where 0.0 means no missing values and 100.0 means all values are missing.

Examples

>>> import ray
>>> from ray.data.aggregate import MissingValuePercentage
>>> # Create a dataset with some missing values
>>> ds = ray.data.from_items([
...     {"value": 1}, {"value": None}, {"value": 3},
...     {"value": None}, {"value": 5}
... ])
>>> # Calculate missing value percentage
>>> result = ds.aggregate(MissingValuePercentage(on="value"))
>>> # result: 40.0 (2 out of 5 values are missing)
>>> # Using with groupby
>>> ds = ray.data.from_items([
...     {"group": "A", "value": 1}, {"group": "A", "value": None},
...     {"group": "B", "value": 3}, {"group": "B", "value": None}
... ])
>>> result = ds.groupby("group").aggregate(MissingValuePercentage(on="value")).take_all()
>>> # result: [{'group': 'A', 'missing_pct(value)': 50.0},
>>> #          {'group': 'B', 'missing_pct(value)': 50.0}]

Parameters:

• on (str) – The name of the column to calculate missing value percentage on.

• alias_name (str | None) – Optional name for the resulting column. If not provided, defaults to “missing_pct({column_name})”.

PublicAPI (alpha): This API is in alpha and may change before becoming stable.

Methods

get_agg_name	Return the agg name (e.g., 'sum', 'mean', 'count').
output_field	Return the PyArrow Field this aggregator produces.