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 (VariableMeasureConvertible) – 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. WhenFalse
, 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.