atoti.rank()#

atoti.rank(measure, hierarchy, /, *, ascending=True, apply_filters=True)#

Return a measure equal to the rank of a hierarchy’s members according to a reference measure.

Members with equal values are further ranked using the level order.

Parameters:

  • measure (NonConstantMeasureConvertible) – The measure on which the ranking is done.

  • hierarchy (Hierarchy) – The hierarchy containing the members to rank.

  • ascending (bool) – When set to False, the 1st place goes to the member with greatest value.

  • apply_filters (bool) – When True, query filters on the given hierarchy will be applied before ranking members. When False, query filters on the given hierarchy will be applied after the ranking, resulting in “holes” in the ranks.

Return type:

MeasureDescription

Example

>>> df = pd.DataFrame(
...     columns=["Year", "Month", "Day", "Quantity"],
...     data=[
...         (2000, 1, 1, 15),
...         (2000, 1, 2, 10),
...         (2000, 2, 1, 30),
...         (2000, 2, 2, 20),
...         (2000, 2, 5, 30),
...         (2000, 4, 4, 5),
...         (2000, 4, 5, 10),
...         (2020, 12, 6, 15),
...         (2020, 12, 7, 15),
...     ],
... )
>>> table = session.read_pandas(
...     df, table_name="Rank", 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["Rank"] = tt.rank(m["Quantity.SUM"], h["Date"])
>>> cube.query(
...     m["Quantity.SUM"],
...     m["Rank"],
...     levels=[l["Day"]],
...     include_totals=True,
... )
                Quantity.SUM Rank
Year  Month Day
Total                    150    1
2000                     120    2
      1                   25    2
            1             15    2
            2             10    1
      2                   80    3
            1             30    2
            2             20    1
            5             30    3
      4                   15    1
            4              5    1
            5             10    2
2020                      30    1
      12                  30    1
            6             15    1
            7             15    2
>>> m["Rank with filters not applied"] = tt.rank(
...     m["Quantity.SUM"], h["Date"], apply_filters=False
... )
>>> cube.query(
...     m["Quantity.SUM"],
...     m["Rank"],
...     m["Rank with filters not applied"],
...     levels=[l["Month"]],
...     include_totals=True,
...     filter=l["Year"] == "2000",
... )
            Quantity.SUM Rank Rank with filters not applied
Year  Month
Total                120    1                             1
2000                 120    1                             2
      1               25    2                             2
      2               80    3                             3
      4               15    1                             1

2000-01-01 and 2000-01-05 have the same Quantity.SUM value so they’re ranked according to l[“Day”].order.