HyukjinKwon commented on a change in pull request #25352:
[SPARK-28422][SQL][Python] GROUPED_AGG pandas_udf should work without group by
clause
URL: https://github.com/apache/spark/pull/25352#discussion_r313466840
##########
File path: python/pyspark/sql/tests/test_pandas_udf_grouped_agg.py
##########
@@ -474,6 +474,21 @@ def test_grouped_with_empty_partition(self):
result = df.groupBy('id').agg(f(df['x']).alias('sum')).collect()
self.assertEqual(result, expected)
+ def test_grouped_without_group_by_clause(self):
+ @pandas_udf('double', PandasUDFType.GROUPED_AGG)
+ def max_udf(v):
+ return v.max()
+
+ df = self.spark.range(0, 100)
+ self.spark.udf.register('max_udf', max_udf)
+
+ with self.tempView("table"):
+ df.createTempView('table')
+
+ agg1 = df.agg(max_udf(df['id']))
+ agg2 = self.spark.sql("select max_udf(id) from table")
+ assert_frame_equal(agg1.toPandas(), agg2.toPandas())
Review comment:
nit: we can just collect and compare the value without `toPandas` but not a
bit deal.
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