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final class atoti.CumulativeScope

Scope performing a cumulative aggregation.

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Example

Using this scope with atoti.agg.sum() to perform a cumulative sum (also called running sum or prefix sum): 
>>> from datetime import date
>>> df = pd.DataFrame(
...     columns=["Date", "Quantity"],
...     data=[
...         (date(2019, 7, 1), 15),
...         (date(2019, 7, 2), 20),
...         (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="Cumulative")
>>> cube = session.create_cube(table)
>>> h, l, m = cube.hierarchies, cube.levels, cube.measures
>>> cube.create_date_hierarchy("Date", column=table["Date"])
>>> h["Date"] = {**h["Date"], "Date": table["Date"]}
>>> m["Quantity.SUM"] = tt.agg.sum(table["Quantity"])
>>> m["Cumulative quantity"] = tt.agg.sum(
...     m["Quantity.SUM"], scope=tt.CumulativeScope(l["Day"])
... )
>>> cube.query(
...     m["Quantity.SUM"],
...     m["Cumulative quantity"],
...     levels=[l["Day"]],
...     include_totals=True,
... )
                Quantity.SUM Cumulative quantity
Year  Month Day
Total                    110                 110
2018                      35                  35
      6                   20                  20
            1             15                  15
            2              5                  20
      7                   15                  35
            1              5                  25
            2             10                  35
2019                      75                 110
      6                   40                  75
            1             25                  60
            2             15                  75
      7                   35                 110
            1             15                  90
            2             20                 110
Using dense: 
>>> m["Quantity L"] = tt.where(m["Quantity.SUM"] > 10, m["Quantity.SUM"])
>>> m["Cumulative quantity L"] = tt.agg.sum(
...     m["Quantity L"], scope=tt.CumulativeScope(l["Day"])
... )
>>> m["Dense cumulative quantity L"] = tt.agg.sum(
...     m["Quantity L"], scope=tt.CumulativeScope(l["Day"], dense=True)
... )
>>> cube.query(
...     m["Quantity.SUM"],
...     m["Quantity L"],
...     m["Cumulative quantity L"],
...     m["Dense cumulative quantity L"],
...     levels=[l["Day"]],
...     include_totals=True,
... )
                Quantity.SUM Quantity L Cumulative quantity L Dense cumulative quantity L
Year  Month Day
Total                    110        110                    90                          90
2018                      35         35                    15                          15
      6                   20         20                    15                          15
            1             15         15                    15                          15
            2              5                                                           15
      7                   15         15                                                15
            1              5                                                           15
            2             10                                                           15
2019                      75         75                    90                          90
      6                   40         40                    55                          55
            1             25         25                    40                          40
            2             15         15                    55                          55
      7                   35         35                    90                          90
            1             15         15                    70                          70
            2             20         20                    90                          90
Using partitioning: 
>>> m["Partitioned by month"] = tt.agg.sum(
...     m["Quantity.SUM"],
...     scope=tt.CumulativeScope(l["Day"], partitioning=l["Month"]),
... )
>>> cube.query(
...     m["Quantity.SUM"],
...     m["Partitioned by month"],
...     levels=[l["Day"]],
...     include_totals=True,
... )
                Quantity.SUM Partitioned by month
Year  Month Day
Total                    110
2018                      35
      6                   20                   20
            1             15                   15
            2              5                   20
      7                   15                   15
            1              5                    5
            2             10                   15
2019                      75
      6                   40                   40
            1             25                   25
            2             15                   40
      7                   35                   35
            1             15                   15
            2             20                   35
window can be a:
  • range starting with a <=0 value and ending with a >=0 value. 
    >>> m["3 previous members window"] = tt.agg.sum(
...     m["Quantity.SUM"],
...     scope=tt.CumulativeScope(l["Day"], window=range(-3, 0)),
... )
>>> cube.query(
...     m["Quantity.SUM"],
...     m["3 previous members window"],
...     levels=[l["Day"]],
...     include_totals=True,
... )
                Quantity.SUM 3 previous members window
Year  Month Day
Total                    110                        75
2018                      35                        35
      6                   20                        20
            1             15                        15
            2              5                        20
      7                   15                        35
            1              5                        25
            2             10                        35
2019                      75                        75
      6                   40                        55
            1             25                        45
            2             15                        55
      7                   35                        75
            1             15                        65
            2             20                        75
  • time period window as a two-element tuple of either None or a period as specified by Java’s Period.parse(). 
    >>> m["2 days window"] = tt.agg.sum(
...     m["Quantity.SUM"],
...     scope=tt.CumulativeScope(l["Date"], window=("-P2D", None)),
... )
>>> m["2 days window partitioned by month"] = tt.agg.sum(
...     m["Quantity.SUM"],
...     scope=tt.CumulativeScope(
...         level=l["Date"],
...         window=("-P2D", None),
...         partitioning=l["Month"],
...     ),
... )
>>> cube.query(
...     m["Quantity.SUM"],
...     m["2 days window"],
...     m["2 days window partitioned by month"],
...     levels=[l["Day"]],
...     include_totals=True,
... )
                Quantity.SUM 2 days window 2 days window partitioned by month
Year  Month Day
Total                    110            35
2018                      35            15
      6                   20            20                                 20
            1             15            15                                 15
            2              5            20                                 20
      7                   15            15                                 15
            1              5             5                                  5
            2             10            15                                 15
2019                      75            35
      6                   40            40                                 40
            1             25            25                                 25
            2             15            40                                 40
      7                   35            35                                 35
            1             15            15                                 15
            2             20            35                                 35

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dense : bool = False

When True, all members of level, even those with no value for the underlying measure, will be taken into account, possibly leading to repeated values.

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level : HasIdentifier[LevelIdentifier] | LevelIdentifier

The level along which member values are cumulated.

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partitioning : HasIdentifier[LevelIdentifier] | LevelIdentifier | None = None

The level at which to start the aggregation over. If not None, partitioning must part of the same hierarchy as level and be “above” it (i.e. before it in list(hierarchy)).

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window : range | tuple[str, str] | tuple[str | None, str] | tuple[str, str | None] | None = None

The window defining the sliding range selecting members before and after the current one (using level’s order) to be aggregated. Default to range(-∞, 0), meaning that the aggregated value for a given member is computed using all the members before it, itself, and no members after it.