atoti.SiblingsScope

class atoti.SiblingsScope

Scope to perform a “siblings” aggregation.

With a siblings scope, the value for the member of a given level in the hierarchy is computed by taking the contribution of all of the members on the same level (its siblings).

A siblings aggregation is an appropriate tool for operations such as marginal aggregations (marginal VaR, marginal mean) for non-linear aggregation functions.

Example

>>> from datetime import date
>>> df = pd.DataFrame(
...     columns=["Date", "Quantity"],
...     data=[
...         (date(2019, 7, 1), 15),
...         (date(2019, 7, 2), 20),
...         (date(2019, 7, 3), 30),
...         (date(2019, 6, 1), 25),
...         (date(2019, 6, 2), 15),
...         (date(2018, 7, 1), 5),
...         (date(2018, 7, 2), 10),
...         (date(2018, 6, 1), 15),
...         (date(2018, 6, 2), 5),
...     ],
... )
>>> table = session.read_pandas(df, table_name="Siblings")
>>> cube = session.create_cube(table, mode="manual")
>>> h, l, m = cube.hierarchies, cube.levels, cube.measures
>>> cube.create_date_hierarchy("Date", column=table["Date"])
>>> m["Quantity.SUM"] = tt.agg.sum(table["Quantity"])
>>> m["Siblings quantity"] = tt.agg.sum(
...     m["Quantity.SUM"], scope=tt.SiblingsScope(hierarchy=h["Date"])
... )
>>> cube.query(
...     m["Quantity.SUM"],
...     m["Siblings quantity"],
...     levels=[l["Day"]],
...     include_totals=True,
... )
                Quantity.SUM Siblings quantity
Year  Month Day
Total                    140               140
2018                      35               140
      6                   20                35
            1             15                20
            2              5                20
      7                   15                35
            1              5                15
            2             10                15
2019                     105               140
      6                   40               105
            1             25                40
            2             15                40
      7                   65               105
            1             15                65
            2             20                65
            3             30                65

exclude_self: bool = False

Whether to include the current member’s contribution in its cumulative value.

>>> m["Siblings quantity excluding self"] = tt.agg.sum(
...     m["Quantity.SUM"],
...     scope=tt.SiblingsScope(hierarchy=h["Date"], exclude_self=True),
... )
>>> cube.query(
...     m["Quantity.SUM"],
...     m["Siblings quantity excluding self"],
...     levels=[l["Day"]],
...     include_totals=True,
... )
                Quantity.SUM Siblings quantity excluding self
Year  Month Day
Total                    140                                0
2018                      35                              105
      6                   20                               15
            1             15                                5
            2              5                               15
      7                   15                               20
            1              5                               10
            2             10                                5
2019                     105                               35
      6                   40                               65
            1             25                               15
            2             15                               25
      7                   65                               40
            1             15                               50
            2             20                               45
            3             30                               35

hierarchy: HasIdentifier[HierarchyIdentifier] | HierarchyIdentifier

The hierarchy containing the levels along which the aggregation is performed.