pyspark.sql.functions.grouping_id#

pyspark.sql.functions.grouping_id(*cols)[source]#

Aggregate function: returns the level of grouping, equals to

(grouping(c1) << (n-1)) + (grouping(c2) << (n-2)) + … + grouping(cn)

New in version 2.0.0.

Changed in version 3.4.0: Supports Spark Connect.

Parameters

colsColumn or column name: columns to check for.

Returns

Column: returns level of the grouping it relates to.

Notes

The list of columns should match with grouping columns exactly, or empty (means all the grouping columns).

Examples

>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
...     [(1, "a", "a"), (3, "a", "a"), (4, "b", "c")], ["c1", "c2", "c3"])
>>> df.cube("c2", "c3").agg(sf.grouping_id(), sf.sum("c1")).orderBy("c2", "c3").show()
+----+----+-------------+-------+
|  c2|  c3|grouping_id()|sum(c1)|
+----+----+-------------+-------+
|NULL|NULL|            3|      8|
|NULL|   a|            2|      4|
|NULL|   c|            2|      4|
|   a|NULL|            1|      4|
|   a|   a|            0|      4|
|   b|NULL|            1|      4|
|   b|   c|            0|      4|
+----+----+-------------+-------+