pyflink.dataframe.GroupedDataFrame.agg

GroupedDataFrame.agg(*aggs: pyflink.table.expression.Expression, **named_aggs: pyflink.table.expression.Expression) → pyflink.dataframe.dataframe.DataFrame

Apply aggregation expressions.

Parameters

• *aggs – Aggregation expressions.

• **named_aggs – Named aggregation expressions. Each expression is aliased to the corresponding keyword.

Returns

A new DataFrame with aggregation results.

Example::

>>> import pyflink.dataframe as pf
>>> df = pf.from_records(
...     [("A", 1), ("A", 2), ("B", 3)],
...     schema=["category", "value"],
... )
>>> # Group by and aggregate
>>> result = df.group_by("category").agg(
...     pf.col("value").sum.alias("total")
... )
>>> result = df.group_by("category").agg(
...     total=pf.col("value").sum
... )
>>> # result columns: ["category", "total"]