[SPARK-27463][PYTHON][FOLLOW-UP] Miscellaneous documentation and code cleanup of cogroup pandas UDF

python/pyspark/sql/__init__.py

@@ -51,11 +51,12 @@
 from pyspark.sql.group import GroupedData
 from pyspark.sql.readwriter import DataFrameReader, DataFrameWriter
 from pyspark.sql.window import Window, WindowSpec
+from pyspark.sql.cogroup import CoGroupedData
 
 
 __all__ = [
     'SparkSession', 'SQLContext', 'UDFRegistration',
     'DataFrame', 'GroupedData', 'Column', 'Catalog', 'Row',
     'DataFrameNaFunctions', 'DataFrameStatFunctions', 'Window', 'WindowSpec',
-    'DataFrameReader', 'DataFrameWriter'
+    'DataFrameReader', 'DataFrameWriter', 'CoGroupedData'
 ]

python/pyspark/sql/cogroup.py

@@ -14,6 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
+import sys
 
 from pyspark import since
 from pyspark.rdd import PythonEvalType
@@ -43,9 +44,9 @@ def apply(self, udf):
         as a `DataFrame`.
 
         The user-defined function should take two `pandas.DataFrame` and return another
-        `pandas.DataFrame`. For each side of the cogroup, all columns are passed together
-        as a `pandas.DataFrame` to the user-function and the returned `pandas.DataFrame`
-        are combined as a :class:`DataFrame`.
+        `pandas.DataFrame`.  For each side of the cogroup, all columns are passed together as a
+        `pandas.DataFrame` 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.
@@ -61,15 +62,16 @@ def apply(self, udf):
 
         >>> from pyspark.sql.functions import pandas_udf, PandasUDFType
         >>> df1 = spark.createDataFrame(
-        ...    [(20000101, 1, 1.0), (20000101, 2, 2.0), (20000102, 1, 3.0), (20000102, 2, 4.0)],
-        ...    ("time", "id", "v1"))
+        ...     [(20000101, 1, 1.0), (20000101, 2, 2.0), (20000102, 1, 3.0), (20000102, 2, 4.0)],
+        ...     ("time", "id", "v1"))
         >>> df2 = spark.createDataFrame(
-        ... [(20000101, 1, "x"), (20000101, 2, "y")],
-        ... ("time", "id", "v2"))
-        >>> @pandas_udf("time int, id int, v1 double, v2 string", PandasUDFType.COGROUPED_MAP)
+        ...     [(20000101, 1, "x"), (20000101, 2, "y")],
+        ...     ("time", "id", "v2"))
+        >>> @pandas_udf("time int, id int, v1 double, v2 string",
+        ...             PandasUDFType.COGROUPED_MAP)  # doctest: +SKIP
         ... def asof_join(l, r):
         ...     return pd.merge_asof(l, r, on="time", by="id")
-        >>> df1.groupby("id").cogroup(df2.groupby("id")).apply(asof_join).show()
+        >>> df1.groupby("id").cogroup(df2.groupby("id")).apply(asof_join).show()  # doctest: +SKIP
         +--------+---+---+---+
         |    time| id| v1| v2|
         +--------+---+---+---+
@@ -79,6 +81,27 @@ def apply(self, udf):
         |20000102|  2|4.0|  y|
         +--------+---+---+---+
 
+        Alternatively, the user can define a function that takes three arguments.  In this case,
+        the grouping key(s) will be passed as the first argument and the data will be passed as the
+        second and third arguments.  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 two
+        `pandas.DataFrame` containing all columns from the original Spark DataFrames.
+
+        >>> @pandas_udf("time int, id int, v1 double, v2 string",
+        ...             PandasUDFType.COGROUPED_MAP)  # doctest: +SKIP
+        ... def asof_join(k, l, r):
+        ...     if k == (1,):
+        ...         return pd.merge_asof(l, r, on="time", by="id")
+        ...     else:
+        ...         return pd.DataFrame(columns=['time', 'id', 'v1', 'v2'])
+        >>> df1.groupby("id").cogroup(df2.groupby("id")).apply(asof_join).show()  # doctest: +SKIP
+        +--------+---+---+---+
+        |    time| id| v1| v2|
+        +--------+---+---+---+
+        |20000101|  1|1.0|  x|
+        |20000102|  1|3.0|  x|
+        +--------+---+---+---+
+
         .. seealso:: :meth:`pyspark.sql.functions.pandas_udf`
 
         """
@@ -96,3 +119,25 @@ def apply(self, udf):
     def _extract_cols(gd):
         df = gd._df
         return [df[col] for col in df.columns]
+
+
+def _test():
+    import doctest
+    from pyspark.sql import SparkSession
+    import pyspark.sql.cogroup
+    globs = pyspark.sql.cogroup.__dict__.copy()
+    spark = SparkSession.builder\
+        .master("local[4]")\
+        .appName("sql.cogroup tests")\
+        .getOrCreate()
+    globs['spark'] = spark
+    (failure_count, test_count) = doctest.testmod(
+        pyspark.sql.cogroup, globs=globs,
+        optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE | doctest.REPORT_NDIFF)
+    spark.stop()
+    if failure_count:
+        sys.exit(-1)
+
+
+if __name__ == "__main__":
+    _test()

python/pyspark/sql/tests/test_pandas_udf_cogrouped_map.py

@@ -32,14 +32,9 @@
     import pyarrow as pa
 
 
-"""
-Tests below use pd.DataFrame.assign that will infer mixed types (unicode/str) for column names
-from kwargs w/ Python 2, so need to set check_column_type=False and avoid this check
-"""
-if sys.version < '3':
-    _check_column_type = False
-else:
-    _check_column_type = True
+# Tests below use pd.DataFrame.assign that will infer mixed types (unicode/str) for column names
+# From kwargs w/ Python 2, so need to set check_column_type=False and avoid this check
+_check_column_type = sys.version >= '3'
 
 
 @unittest.skipIf(

python/pyspark/sql/tests/test_pandas_udf_grouped_map.py

@@ -37,14 +37,9 @@
     import pyarrow as pa
 
 
-"""
-Tests below use pd.DataFrame.assign that will infer mixed types (unicode/str) for column names
-from kwargs w/ Python 2, so need to set check_column_type=False and avoid this check
-"""
-if sys.version < '3':
-    _check_column_type = False
-else:
-    _check_column_type = True
+# Tests below use pd.DataFrame.assign that will infer mixed types (unicode/str) for column names
+# from kwargs w/ Python 2, so need to set check_column_type=False and avoid this check
+_check_column_type = sys.version >= '3'
 
 
 @unittest.skipIf(

sql/core/src/main/scala/org/apache/spark/sql/RelationalGroupedDataset.scala

@@ -47,8 +47,8 @@ import org.apache.spark.sql.types.{NumericType, StructType}
  */
 @Stable
 class RelationalGroupedDataset protected[sql](
-    val df: DataFrame,
-    val groupingExprs: Seq[Expression],
+    private[sql] val df: DataFrame,
+    private[sql] val groupingExprs: Seq[Expression],
     groupType: RelationalGroupedDataset.GroupType) {
 
   private[this] def toDF(aggExprs: Seq[Expression]): DataFrame = {