ray.data.aggregate.Std#

class ray.data.aggregate.Std(on: str | None = None, ddof: int = 1, ignore_nulls: bool = True, alias_name: str | None = None)[source]#

Bases: AggregateFnV2

Defines standard deviation aggregation.

Uses Welford’s online algorithm for numerical stability. This method computes the standard deviation in a single pass. Results may differ slightly from libraries like NumPy or Pandas that use a two-pass algorithm but are generally more accurate.

See: https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Welford’s_online_algorithm

Example

import ray
from ray.data.aggregate import Std

ds = ray.data.range(100)
# Schema: {'id': int64}
ds = ds.add_column("group_key", lambda x: x % 3)
# Schema: {'id': int64, 'group_key': int64}

# Calculating the standard deviation per group:
result = ds.groupby("group_key").aggregate(Std(on="id")).take_all()
# result: [{'group_key': 0, 'std(id)': ...},
#          {'group_key': 1, 'std(id)': ...},
#          {'group_key': 2, 'std(id)': ...}]
Parameters:

  • on – The name of the column to calculate standard deviation on.

  • ddof – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N is the number of elements. Default is 1.

  • ignore_nulls – Whether to ignore null values. Default is True.

  • alias_name – Optional name for the resulting column.

Methods