atoti.CumulativeScope#
- final class atoti.CumulativeScope#
Scope performing a cumulative aggregation.
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
- dense: bool = False#
When
True
, all members oflevel
, even those with no value for the underlying measure, will be taken into account, possibly leading to repeated values.>>> 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
- level: HasIdentifier[LevelIdentifier] | LevelIdentifier#
The level along which member values are cumulated.
- 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 aslevel
and be “above” it (i.e. before it inlist(hierarchy)
).>>> 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: 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
’sorder
) to be aggregated.The 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 eitherNone
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
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.