pyflink.dataframe.GroupedDataFrame.agg#

GroupedDataFrame.agg(*aggs: pyflink.table.expression.Expression) → pyflink.dataframe.dataframe.DataFrame[source]#

Apply aggregation expressions.

Parameters: *aggs – Aggregation expressions.
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("category"),
...     pf.col("value").sum.alias("total")
... )