xpark.dataset.aggregate.Mean

class xpark.dataset.aggregate.Mean(on: str | None = None, ignore_nulls: bool = True, alias_name: str | None = None)

Defines mean (average) aggregation.

Example

import xpark
from xpark.dataset.aggregate import Mean

ds = xpark.dataset.from_range(100)
# Schema: {'id': int64}
ds = ds.add_column("group_key", lambda x: x % 3)
# Schema: {'id': int64, 'group_key': int64}

# Calculating the mean value per group:
result = ds.groupby("group_key").aggregate(Mean(on="id")).take_all()
# result: [{'group_key': 0, 'mean(id)': ...},
#          {'group_key': 1, 'mean(id)': ...},
#          {'group_key': 2, 'mean(id)': ...}]

Parameters:

• on – The name of the numerical column to calculate the mean on. Must be provided.

• ignore_nulls – Whether to ignore null values. If True (default), nulls are skipped. If False, the mean will be null if any value in the group is null.

• alias_name – Optional name for the resulting column.

Methods

aggregate_block(block)	Aggregates data within a single block.
combine(current_accumulator, new)	Combines a new partial aggregation result with the current accumulator.
finalize(accumulator)	Transforms the final accumulated state into the desired output.
get_agg_name()	Return the agg name (e.g., 'sum', 'mean', 'count').
get_target_column()