- class atoti.Hierarchy#
Hierarchy of a
A hierarchy is a sub category of a
dimensionand represents a precise type of data.
For example, Quarter or Week could be hierarchies in the Time dimension.
- property dimension: str#
Name of the dimension of the hierarchy.
If all the hierarchies in a dimension have their deepest level of type
TIME, the dimension’s type will be set to
TIMEtoo. This can be useful for some clients such as Excel which rely on the dimension’s type to be
TIMEto decide whether to display date filters.
Return a condition to check that the hierarchy is on one of the given members.
hierarchy_1.isin((a,), (b, c))is equivalent to
(level_1 == a) | ((level_1 == b) & (level_2 == c)).
member_paths (Tuple[ConstantValue, ...]) – One or more member paths expressed as tuples on which the hierarchy should be. Each element in a tuple corresponds to a level of the hierarchy, from the shallowest to the deepest.
- Return type
Condition[HierarchyCoordinates, Literal[‘isin’], Constant, None]
>>> df = pd.DataFrame( ... columns=["Country", "City", "Price"], ... data=[ ... ("Germany", "Berlin", 150.0), ... ("Germany", "Hamburg", 120.0), ... ("United Kingdom", "London", 240.0), ... ("United States", "New York", 270.0), ... ("France", "Paris", 200.0), ... ], ... ) >>> table = session.read_pandas( ... df, keys=["Country", "City"], table_name="isin example" ... ) >>> cube = session.create_cube(table) >>> h, l, m = cube.hierarchies, cube.levels, cube.measures >>> h["Geography"] = [l["Country"], l["City"]] >>> m["Price.SUM in Germany and Paris"] = tt.filter( ... m["Price.SUM"], ... h["Geography"].isin(("Germany",), ("France", "Paris")), ... ) >>> cube.query( ... m["Price.SUM"], ... m["Price.SUM in Germany and Paris"], ... levels=[l["Geography", "City"]], ... ) Price.SUM Price.SUM in Germany and Paris Country City France Paris 200.00 200.00 Germany Berlin 150.00 150.00 Hamburg 120.00 120.00 United Kingdom London 240.00 United States New York 270.00
- property slicing: bool#
Whether the hierarchy is slicing or not.
A regular (or non-slicing) hierarchy is considered aggregable, meaning that it makes sense to aggregate data across all members of the hierarchy.
For instance, for a Geography hierarchy, it is useful to see the worldwide aggregated Turnover across all countries.
A slicing hierarchy is not aggregable at the top level, meaning that it does not make sense to aggregate data across all members of the hierarchy.
For instance, for an As of date hierarchy giving the current bank account Balance for a given date, it does provide any meaningful information to aggregate the Balance across all the dates.
- property virtual: bool#
Whether the hierarchy is virtual or not.
A virtual hierarchy is a lightweight hierarchy which does not store in memory the list of its members. It is useful for hierarchies with large cardinality.