pyflink.dataframe.DataFrame.agg#

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

Apply aggregation expressions to the entire DataFrame.

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(
...     [(1, 10), (2, 20), (3, 30)],
...     schema=["order_id", "amount"],
... )
>>> result = df.agg(
...     pf.col("order_id").count.alias("order_count"),
...     pf.col("amount").sum.alias("total_amount"),
... )
>>> result = df.agg(
...     order_count=pf.col("order_id").count,
...     total_amount=pf.col("amount").sum,
... )
>>> # result columns: ["order_count", "total_amount"]