atoti.total()#
- atoti.total(measure, /, *hierarchies)#
Return a measure equal to the passed measure at the top level member on each given hierarchy.
It ignores the filters on this hierarchy.
If the hierarchy is not slicing, total is equal to the value for all the members. If the hierarchy is slicing, total is equal to the value on the first level.
- Parameters:
measure (VariableMeasureConvertible) – The measure to take the total of.
hierarchies (Hierarchy) – The hierarchies on which to find the top-level member.
- Return type:
MeasureDescription
Example
>>> df = pd.DataFrame( ... columns=["Year", "Month", "Day", "Price"], ... data=[ ... (2019, 7, 1, 15.0), ... (2019, 7, 2, 20.0), ... (2019, 6, 1, 25.0), ... (2019, 6, 2, 15.0), ... (2018, 7, 1, 5.0), ... (2018, 7, 2, 10.0), ... (2018, 6, 1, 15.0), ... (2018, 6, 2, 5.0), ... ], ... ) >>> table = session.read_pandas( ... df, table_name="Total", 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["Total(Price)"] = tt.total(m["Price.SUM"], h["Date"]) >>> cube.query( ... m["Price.SUM"], ... m["Total(Price)"], ... levels=[l["Day"]], ... include_totals=True, ... ) Price.SUM Total(Price) Year Month Day Total 110.00 110.00 2018 35.00 110.00 6 20.00 110.00 1 15.00 110.00 2 5.00 110.00 7 15.00 110.00 1 5.00 110.00 2 10.00 110.00 2019 75.00 110.00 6 40.00 110.00 1 25.00 110.00 2 15.00 110.00 7 35.00 110.00 1 15.00 110.00 2 20.00 110.00 >>> h["Date"].slicing = True >>> cube.query( ... m["Price.SUM"], ... m["Total(Price)"], ... levels=[l["Day"]], ... include_totals=True, ... ) Price.SUM Total(Price) Year Month Day 2018 6 1 15.00 35.00 2 5.00 35.00 7 1 5.00 35.00 2 10.00 35.00 2019 6 1 25.00 75.00 2 15.00 75.00 7 1 15.00 75.00 2 20.00 75.00