khalidmammadov commented on code in PR #37662:
URL: https://github.com/apache/spark/pull/37662#discussion_r955964936
##########
python/pyspark/sql/functions.py:
##########
@@ -2377,6 +2672,16 @@ def grouping_id(*cols: "ColumnOrName") -> Column:
The list of columns should match with grouping columns exactly, or empty
(means all
the grouping columns).
+ Parameters
+ ----------
+ cols : :class:`~pyspark.sql.Column` or str
+ columns to check for.
+
+ Returns
+ -------
+ :class:`~pyspark.sql.Column`
+ returns level of the grouping it relates to.
+
Examples
--------
>>> df.cube("name").agg(grouping_id(), sum("age")).orderBy("name").show()
Review Comment:
Done
##########
python/pyspark/sql/functions.py:
##########
@@ -2440,6 +2804,38 @@ def last(col: "ColumnOrName", ignorenulls: bool = False)
-> Column:
-----
The function is non-deterministic because its results depends on the order
of the
rows which may be non-deterministic after a shuffle.
+
+ Parameters
+ ----------
+ col : :class:`~pyspark.sql.Column` or str
+ column to fetch last value for.
+ ignorenulls : :class:`~pyspark.sql.Column` or str
+ if last value is null then look for non-null value.
+
+ Returns
+ -------
+ :class:`~pyspark.sql.Column`
+ last value of the group.
+
+ Examples
+ --------
+ >>> df = spark.createDataFrame([("Alice", 2), ("Bob", 5), ("Alice",
None)], ("name", "age"))
+ >>> df = df.orderBy(df.age.desc())
+ >>> df.groupby("name").agg(last("age")).orderBy("name").show()
+ +-----+---------+
+ | name|last(age)|
+ +-----+---------+
+ |Alice| null|
+ | Bob| 5|
+ +-----+---------+
+
+ >>> df.groupby("name").agg(last("age", True)).orderBy("name").show()
Review Comment:
Done
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