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Cube.create_parameter_hierarchy_from_members(name, members, *, data_type=None, index_measure_name=None)
Create a single-level hierarchy with the given members.
It can be used as a parameter hierarchy in advanced analyzes.
- Parameters:
- name (str) – The name of hierarchy and its single level.
- members (Sequence *[*Constant ]) – The members of the hierarchy.
- data_type (DataType | None) – The type with which the members will be stored.
Automatically inferred by default.
- index_measure_name (str | None) – The name of the indexing measure to create for this hierarchy, if any.
- Return type:
None
Example
>>> df = pd.DataFrame(
... {
... "Seller": ["Seller_1", "Seller_2", "Seller_3"],
... "Prices": [
... [2.5, 49.99, 3.0, 54.99],
... [2.6, 50.99, 2.8, 57.99],
... [2.99, 44.99, 3.6, 59.99],
... ],
... }
... )
>>> table = session.read_pandas(df, table_name="Seller prices")
>>> cube = session.create_cube(table)
>>> l, m = cube.levels, cube.measures
>>> cube.create_parameter_hierarchy_from_members(
... "ProductID",
... ["aBk3", "ceJ4", "aBk5", "ceJ9"],
... index_measure_name="Product index",
... )
>>> m["Prices"] = tt.agg.single_value(table["Prices"])
>>> m["Product price"] = m["Prices"][m["Product index"]]
>>> cube.query(
... m["Product price"],
... levels=[l["Seller"], l["ProductID"]],
... )
Product price
Seller ProductID
Seller_1 aBk3 2.50
aBk5 3.00
ceJ4 49.99
ceJ9 54.99
Seller_2 aBk3 2.60
aBk5 2.80
ceJ4 50.99
ceJ9 57.99
Seller_3 aBk3 2.99
aBk5 3.60
ceJ4 44.99
ceJ9 59.99
