nxcals.api.extraction.data.builders.DataFrame.cube

DataFrame.cube(*cols: ColumnOrName) → GroupedData

DataFrame.cube(__cols: Union[List[Column], List[str]]) → GroupedData

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:

cols (list, 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:

Cube of the data by given columns.

Return type:

GroupedData

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|
+-----+----+-----+