>>> 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, ...}
