uros-b commented on code in PR #57199:
URL: https://github.com/apache/spark/pull/57199#discussion_r3583221227
##########
python/pyspark/sql/column.py:
##########
@@ -1578,6 +1578,24 @@ def outer(self) -> "Column":
--------
pyspark.sql.dataframe.DataFrame.scalar
pyspark.sql.dataframe.DataFrame.exists
+
+ Examples
+ --------
+ >>> from pyspark.sql import functions as sf
+ >>> employees = spark.createDataFrame(
+ ... [(1, "Alice", 5000, 101), (2, "Bob", 6000, 101), (3,
"Charlie", 4500, 102)],
+ ... ["id", "name", "salary", "department_id"],
+ ... )
+ >>> employees.alias("e1").where(
+ ... sf.col("salary") > employees.alias("e2").where(
+ ... sf.col("e2.department_id") ==
sf.col("e1.department_id").outer()
+ ... ).select(sf.avg("salary")).scalar()
+ ... ).select("name", "salary", "department_id").orderBy("name").show()
+ +----+------+-------------+
+ |name|salary|department_id|
+ +----+------+-------------+
+ | Bob| 6000| 101|
+ +----+------+-------------+
Review Comment:
Nit: the 3-row dataset puts a single employee (Charlie, 4500) in dept 102,
so avg == 4500 and Charlie fails the strict > filter, leaving dept 102 with no
qualifying row. The shown output is a single row (Bob), which a reader may find
slightly opaque (why are two of three names absent?). Consider mirroring
DataFrame.scalar() Example 2's richer dataset so both departments yield at
least one qualifying row. The math is correct, this is just a readability
suggestion.
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