grundprinzip commented on code in PR #38883:
URL: https://github.com/apache/spark/pull/38883#discussion_r1040470566
##########
python/pyspark/sql/connect/dataframe.py:
##########
@@ -55,8 +59,109 @@ def __init__(self, df: "DataFrame", *grouping_cols:
Union[Column, str]) -> None:
self._df = df
self._grouping_cols = [x if isinstance(x, Column) else df[x] for x in
grouping_cols]
- def agg(self, measures: Sequence[Column]) -> "DataFrame":
- assert len(measures) > 0, "exprs should not be empty"
+ @overload
+ def agg(self, *exprs: Column) -> "DataFrame":
+ ...
+
+ @overload
+ def agg(self, __exprs: Dict[str, str]) -> "DataFrame":
+ ...
+
+ def agg(self, *exprs: Union[Column, Dict[str, str]]) -> "DataFrame":
+ """Compute aggregates and returns the result as a :class:`DataFrame`.
+
+ The available aggregate functions can be:
+
+ 1. built-in aggregation functions, such as `avg`, `max`, `min`, `sum`,
`count`
+
+ 2. group aggregate pandas UDFs, created with
:func:`pyspark.sql.functions.pandas_udf`
+
+ .. note:: There is no partial aggregation with group aggregate
UDFs, i.e.,
+ a full shuffle is required. Also, all the data of a group will
be loaded into
+ memory, so the user should be aware of the potential OOM risk
if data is skewed
+ and certain groups are too large to fit in memory.
+
+ .. seealso:: :func:`pyspark.sql.functions.pandas_udf`
+
+ If ``exprs`` is a single :class:`dict` mapping from string to string,
then the key
+ is the column to perform aggregation on, and the value is the
aggregate function.
+
+ Alternatively, ``exprs`` can also be a list of aggregate
:class:`Column` expressions.
+
+ .. versionadded:: 1.3.0
Review Comment:
Done
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