pyspark.sql.DataFrame.cube

DataFrame.cube(*cols: ColumnOrName) → GroupedData[source]

Create a multi-dimensional cube for the current DataFrame using the specified columns, so we can run aggregations on them.

New in version 1.4.0.

Changed in version 3.4.0: Supports Spark Connect.

Parameters
colslist, str or Column

columns to create cube by. Each element should be a column name (string) or an expression (Column) or list of them.

Returns
GroupedData

Cube of the data by given columns.

Examples

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

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