atoti.scope.cumulative_scope module#
- class atoti.CumulativeScope#
Scope to perform a cumulative aggregation.
Cumulative aggregations include cumulative sums (also called running sum or prefix sum), mean, min, max, etc.
Example
>>> df = pd.DataFrame( ... columns=["Year", "Month", "Day", "Quantity"], ... data=[ ... (2019, 7, 1, 15), ... (2019, 7, 2, 20), ... (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="Cumulative", ... default_values={"Year": 0, "Month": 0, "Day": 0}, ... ) >>> cube = session.create_cube(table) >>> 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["Cumulative quantity"] = tt.agg.sum( ... m["Quantity.SUM"], scope=tt.CumulativeScope(level=l["Day"]) ... ) >>> m["Cumulative quantity partitioned by month"] = tt.agg.sum( ... m["Quantity.SUM"], ... scope=tt.CumulativeScope(level=l["Day"], partitioning=l["Month"]), ... ) >>> cube.query( ... m["Quantity.SUM"], ... m["Cumulative quantity"], ... m["Cumulative quantity partitioned by month"], ... levels=[l["Day"]], ... include_totals=True, ... ) Quantity.SUM Cumulative quantity Cumulative quantity partitioned by month Year Month Day Total 110 110 2018 35 35 6 20 20 20 1 15 15 15 2 5 20 20 7 15 35 15 1 5 25 5 2 10 35 15 2019 75 110 6 40 75 40 1 25 60 25 2 15 75 40 7 35 110 35 1 15 90 15 2 20 110 35
- __init__(*, level, dense=False, partitioning=None, window=None)#
- Parameters
level (
Level
) – The level along which the aggregation is performed.dense (
bool
) – WhenTrue
, all members of the level, even those with no value for the underlying measure, will be taken into account for the cumulative aggregation (resulting in repeating values).partitioning (
Optional
[Level
]) – The levels in the hierarchy at which to start the aggregation over.window (
Union
[range
,None
,Tuple
[str
,str
],Tuple
[Optional
[str
],str
],Tuple
[str
,Optional
[str
]]]) –The custom aggregation window. The window defines the set of members before and after a given member (using the level order) to be considered in the computation of the cumulative aggregation.
The window can be a:
range
starting with a <=0 value and ending with a >=0 value.By default the window is
range(-∞, 0)
, meaning that the value for a given member is computed using all of the members before it and none after it.For instance, to compute the sliding mean on the 5 previous members of a level:
m2 = atoti.agg.mean(m1, scope=tt.CumulativeScope(level=l["date"], window=range(-5, 0)))
time period window as a two-element tuple of either
None
or a period as specified by Java’s Period.parse().Example:
>>> from datetime import date >>> df = pd.DataFrame( ... columns=["Date", "Quantity"], ... data=[ ... (date(2019, 7, 2), 15), ... (date(2019, 7, 1), 20), ... (date(2019, 6, 1), 25), ... (date(2019, 6, 2), 15), ... (date(2019, 6, 30), 5), ... ], ... ) >>> table = session.read_pandas( ... df, table_name="CumulativeTimePeriod" ... ) >>> cube = session.create_cube(table, mode="manual") >>> h, l, m = cube.hierarchies, cube.levels, cube.measures >>> cube.create_date_hierarchy("Date", column=table["Date"]) >>> h["Date"] = { ... **h["Date"].levels, ... "Date": table["Date"], ... } >>> m["Quantity.SUM"] = tt.agg.sum(table["Quantity"]) >>> m["Cumulative quantity"] = tt.agg.sum( ... m["Quantity.SUM"], scope=tt.CumulativeScope(level=l["Date"]) ... ) >>> m["Cumulative quantity with 2 days window"] = tt.agg.sum( ... m["Quantity.SUM"], ... scope=tt.CumulativeScope( ... level=l["Date"], window=("-P2D", None) ... ), ... ) >>> m[ ... "Cumulative quantity with 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["Cumulative quantity"], ... m["Cumulative quantity with 2 days window"], ... m[ ... "Cumulative quantity with 2 days window partitioned by month" ... ], ... levels=[l["Day"]], ... include_totals=True, ... ) Quantity.SUM Cumulative quantity Cumulative quantity with 2 days window Cumulative quantity with 2 days window partitioned by month Year Month Day Total 80 80 35 2019 80 80 35 6 45 45 5 5 1 25 25 25 25 2 15 40 40 40 30 5 45 5 5 7 35 80 35 35 1 20 65 25 20 2 15 80 35 35