atoti.agg.sum_product()#
- atoti.agg.sum_product(*factors: Column) MeasureDescription #
- atoti.agg.sum_product(*factors: MeasureConvertible, scope: CumulativeScope | SiblingsScope | OriginScope) MeasureDescription
Return a measure equal to the sum product aggregation of the passed factors across the specified scope.
- Parameters:
factors – The factors to multiply together and then aggregate as a sum.
scope – The
aggregation scope
.
Example
>>> from datetime import date >>> df = pd.DataFrame( ... columns=["Date", "Category", "Price", "Quantity", "Array"], ... data=[ ... (date(2020, 1, 1), "TV", 300.0, 5, [10.0, 15.0]), ... (date(2020, 1, 2), "TV", 200.0, 1, [5.0, 15.0]), ... (date(2020, 1, 1), "Computer", 900.0, 2, [2.0, 3.0]), ... (date(2020, 1, 2), "Computer", 800.0, 3, [10.0, 20.0]), ... (date(2020, 1, 1), "TV", 500.0, 2, [3.0, 10.0]), ... ], ... ) >>> table = session.read_pandas( ... df, ... table_name="Date", ... ) >>> table.head().sort_values(["Category", "Date"]).reset_index(drop=True) Date Category Price Quantity Array 0 2020-01-01 Computer 900.0 2 [2.0, 3.0] 1 2020-01-02 Computer 800.0 3 [10.0, 20.0] 2 2020-01-01 TV 300.0 5 [10.0, 15.0] 3 2020-01-01 TV 500.0 2 [3.0, 10.0] 4 2020-01-02 TV 200.0 1 [5.0, 15.0] >>> cube = session.create_cube(table) >>> h, l, m = cube.hierarchies, cube.levels, cube.measures >>> m["turnover"] = tt.agg.sum_product(table["Price"], table["Quantity"]) >>> cube.query(m["turnover"], levels=[l["Category"]]) turnover Category Computer 4,200.00 TV 2,700.00 >>> m["array sum product"] = tt.agg.sum_product(table["Price"], table["Array"]) >>> cube.query(m["array sum product"]) array sum product 0 doubleVector[2]{15300.0, ...}