atoti.agg.distinct()

@overload
atoti.agg.distinct(
operand: LevelOrVariableColumnConvertible,
/,
) → MeasureDefinition

@overload
atoti.agg.distinct(
    operand: VariableMeasureConvertible,
    /,
    *,
    scope: CumulativeScope | SiblingsScope | OriginScope,
) → MeasureDefinition

Return an array measure equal to the distinct values of the passed measure across the specified scope.

This feature is experimental, its key is "agg.distinct".

Parameters

operand

The measure or table column to aggregate.

scope

The aggregation scope.

>>> df = pd.DataFrame(
...     columns=["ID", "City"],
...     data=[
...         (1, "Paris"),
...         (2, "London"),
...         (3, "New York"),
...         (4, "Paris"),
...     ],
... )
>>> table = session.read_pandas(df, keys={"ID"}, table_name="Example")
>>> cube = session.create_cube(table)
>>> l, m = cube.levels, cube.measures
>>> with tt.experimental({"agg.distinct"}):
...     m["Distinct cities"] = tt.agg.distinct(table["City"])
>>> m["Distinct cities"].formatter = "ARRAY[',']"
>>> cube.query(m["Distinct cities"], levels=[l["City"]], include_totals=True)
                Distinct cities
City
Total     New York,London,Paris
London                   London
New York               New York
Paris                     Paris