cube

Create a multi-dimensional cube for the current DataFrame using the specified columns, allowing aggregations to be performed on them.

Syntax

cube(*cols: "ColumnOrName")

Parameters

Parameter	Type	Description
cols	list, str, int or Column	The columns to cube by. Each element should be a column name (string) or an expression (Column) or a column ordinal (int, 1-based) or list of them.

Returns

GroupedData: Cube of the data based on the specified columns.

Notes

A column ordinal starts from 1, which is different from the 0-based __getitem__.

Examples

Python

df = spark.createDataFrame([("Alice", 2), ("Bob", 5)], schema=["name", "age"])

df.cube("name").count().orderBy("name").show()
# +-----+-----+
# | name|count|
# +-----+-----+
# | NULL|    2|
# |Alice|    1|
# |  Bob|    1|
# +-----+-----+

df.cube("name", df.age).count().orderBy("name", "age").show()
# +-----+----+-----+
# | name| age|count|
# +-----+----+-----+
# | NULL|NULL|    2|
# | NULL|   2|    1|
# | NULL|   5|    1|
# |Alice|NULL|    1|
# |Alice|   2|    1|
# |  Bob|NULL|    1|
# |  Bob|   5|    1|
# +-----+----+-----+