diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index b3d221495d621..c9e35c0a33921 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -2161,7 +2161,7 @@ def pandas_udf(f=None, returnType=None, functionType=None):
     1. SCALAR
 
        A scalar UDF defines a transformation: One or more `pandas.Series` -> A `pandas.Series`.
-       The returnType should be a primitive data type, e.g., `DoubleType()`.
+       The returnType should be a primitive data type, e.g., :class:`DoubleType`.
        The length of the returned `pandas.Series` must be of the same as the input `pandas.Series`.
 
        Scalar UDFs are used with :meth:`pyspark.sql.DataFrame.withColumn` and
@@ -2226,11 +2226,12 @@ def pandas_udf(f=None, returnType=None, functionType=None):
     3. GROUP_AGG
 
        A group aggregate UDF defines a transformation: One or more `pandas.Series` -> A scalar
-       The returnType should be a primitive data type, e.g, `DoubleType()`.
+       The `returnType` should be a primitive data type, e.g, :class:`DoubleType`.
        The returned scalar can be either a python primitive type, e.g., `int` or `float`
        or a numpy data type, e.g., `numpy.int64` or `numpy.float64`.
 
-       StructType and ArrayType are currently not supported.
+       :class:`ArrayType`, :class:`MapType` and :class:`StructType` are currently not supported as
+       output types.
 
        Group aggregate UDFs are used with :meth:`pyspark.sql.GroupedData.agg`
 
@@ -2249,9 +2250,6 @@ def pandas_udf(f=None, returnType=None, functionType=None):
        |  2|        6.0|
        +---+-----------+
 
-       .. note:: There is no partial aggregation with group aggregate UDFs, i.e.,
-           a full shuffle is required.
-
        .. seealso:: :meth:`pyspark.sql.GroupedData.agg`
 
     .. note:: The user-defined functions are considered deterministic by default. Due to
diff --git a/python/pyspark/sql/group.py b/python/pyspark/sql/group.py
index 96dace079b353..fa71abf59ff7c 100644
--- a/python/pyspark/sql/group.py
+++ b/python/pyspark/sql/group.py
@@ -69,18 +69,23 @@ def agg(self, *exprs):
 
         1. built-in aggregation functions, such as `avg`, `max`, `min`, `sum`, `count`
 
-        2. group aggregate pandas UDFs
+        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.
+               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:: :meth:`pyspark.sql.functions.pandas_udf`
+           .. 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.
 
+        .. note:: Built-in aggregation functions and group aggregate pandas UDFs cannot be mixed
+            in a single call to this function.
+
         :param exprs: a dict mapping from column name (string) to aggregate functions (string),
             or a list of :class:`Column`.
 
@@ -220,16 +225,18 @@ def apply(self, udf):
 
         The user-defined function should take a `pandas.DataFrame` and return another
         `pandas.DataFrame`. For each group, all columns are passed together as a `pandas.DataFrame`
-        to the user-function and the returned `pandas.DataFrame`s are combined as a
+        to the user-function and the returned `pandas.DataFrame` are combined as a
         :class:`DataFrame`.
+
         The returned `pandas.DataFrame` can be of arbitrary length and its schema must match the
         returnType of the pandas udf.
 
-        This function does not support partial aggregation, and requires shuffling all the data in
-        the :class:`DataFrame`.
+        .. note:: This function requires a full shuffle. 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.
 
         :param udf: a group map user-defined function returned by
-            :meth:`pyspark.sql.functions.pandas_udf`.
+            :func:`pyspark.sql.functions.pandas_udf`.
 
         >>> from pyspark.sql.functions import pandas_udf, PandasUDFType
         >>> df = spark.createDataFrame(
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 1543d6d1fe78b..7661a6406dd83 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -381,7 +381,7 @@ abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
           resultExpressions,
           planLater(child)))
 
-      case PhysicalAggregation(groupingExpressions, aggExpressions, resultExpressions, child) =>
+      case PhysicalAggregation(_) =>
         // If cannot match the two cases above, then it's an error
         throw new AnalysisException(
           "Cannot use a mixture of aggregate function and group aggregate pandas UDF")