atoti.scope.origin module

atoti.scope.origin(*levels)

Create an aggregation scope with an arbitrary number of levels.

The passed levels define a boundary above and under which the aggregation is performed differently. When those levels are not expressed in a query, the measure will drill down until finding the value for all members of these levels, and then aggregate those values using the user-defined aggregation function. This allows to compute measures that show the yearly mean when looking at the grand total, but the sum of each month’s value when looking at each year individually.

Parameters

levels (Level) – The levels defining the dynamic aggregation domain.

Example

>>> df = pd.DataFrame(
...     columns=["Year", "Month", "Day", "Quantity"],
...     data=[
...         (2019, 7, 1, 15),
...         (2019, 7, 2, 20),
...         (2019, 7, 3, 30),
...         (2019, 6, 1, 25),
...         (2019, 6, 2, 15),
...         (2018, 7, 1, 5),
...         (2018, 7, 2, 10),
...         (2018, 6, 1, 15),
...         (2018, 6, 2, 5),
...     ],
... )
>>> table = session.read_pandas(df, table_name="Origin")
>>> cube = session.create_cube(table, mode="manual")
>>> h, l, m = cube.hierarchies, cube.levels, cube.measures
>>> h["Date"] = [table["Year"], table["Month"], table["Day"]]
>>> m["Quantity.SUM"] = tt.agg.sum(table["Quantity"])
>>> m["Average of monthly quantities"] = tt.agg.mean(
...     m["Quantity.SUM"], scope=tt.scope.origin(l["Month"])
... )
>>> cube.query(
...     m["Quantity.SUM"],
...     m["Average of monthly quantities"],
...     levels=[l["Day"]],
...     include_totals=True,
... )
                Quantity.SUM Average of monthly quantities
Year  Month Day
Total                    140                         35.00
2018                      35                         17.50
      6                   20                         20.00
            1             15                         15.00
            2              5                          5.00
      7                   15                         15.00
            1              5                          5.00
            2             10                         10.00
2019                     105                         52.50
      6                   40                         40.00
            1             25                         25.00
            2             15                         15.00
      7                   65                         65.00
            1             15                         15.00
            2             20                         20.00
            3             30                         30.00

Return type

Scope