atoti.function.total module

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 (MeasureDescription) – The measure to take the total of.

• hierarchies (Hierarchy) – The hierarchies on which to find the top-level member.

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

Return type

MeasureDescription