diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index d58d8d10e5cd..e93b55d6ae12 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -2781,14 +2781,14 @@ def pandas_udf(f=None, returnType=None, functionType=None):
        +---+-------------------+
 
        Alternatively, the user can define a function that takes two arguments.
-       In this case, the grouping key will be passed as the first argument and the data will
-       be passed as the second argument. The grouping key will be passed as a tuple of numpy
+       In this case, the grouping key(s) will be passed as the first argument and the data will
+       be passed as the second argument. The grouping key(s) will be passed as a tuple of numpy
        data types, e.g., `numpy.int32` and `numpy.float64`. The data will still be passed in
        as a `pandas.DataFrame` containing all columns from the original Spark DataFrame.
-       This is useful when the user does not want to hardcode grouping key in the function.
+       This is useful when the user does not want to hardcode grouping key(s) in the function.
 
-       >>> from pyspark.sql.functions import pandas_udf, PandasUDFType
        >>> import pandas as pd  # doctest: +SKIP
+       >>> from pyspark.sql.functions import pandas_udf, PandasUDFType
        >>> df = spark.createDataFrame(
        ...     [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)],
        ...     ("id", "v"))  # doctest: +SKIP
@@ -2804,6 +2804,22 @@ def pandas_udf(f=None, returnType=None, functionType=None):
        |  1|1.5|
        |  2|6.0|
        +---+---+
+       >>> @pandas_udf(
+       ...    "id long, `ceil(v / 2)` long, v double",
+       ...    PandasUDFType.GROUPED_MAP)  # doctest: +SKIP
+       >>> def sum_udf(key, pdf):
+       ...     # key is a tuple of two numpy.int64s, which is the values
+       ...     # of 'id' and 'ceil(df.v / 2)' for the current group
+       ...     return pd.DataFrame([key + (pdf.v.sum(),)])
+       >>> df.groupby(df.id, ceil(df.v / 2)).apply(sum_udf).show()  # doctest: +SKIP
+       +---+-----------+----+
+       | id|ceil(v / 2)|   v|
+       +---+-----------+----+
+       |  2|          5|10.0|
+       |  1|          1| 3.0|
+       |  2|          3| 5.0|
+       |  2|          2| 3.0|
+       +---+-----------+----+
 
        .. note:: If returning a new `pandas.DataFrame` constructed with a dictionary, it is
            recommended to explicitly index the columns by name to ensure the positions are correct,