switch()

>>> df = pd.DataFrame(
...     columns=["Id", "City", "Value"],
...     data=[
...         (0, "Paris", 1.0),
...         (1, "Paris", 2.0),
...         (2, "London", 3.0),
...         (3, "London", 4.0),
...         (4, "Paris", 5.0),
...         (5, "Singapore", 7.0),
...         (6, "NYC", 2.0),
...     ],
... )
>>> table = session.read_pandas(df, keys={"Id"}, table_name="Switch example")
>>> cube = session.create_cube(table)
>>> l, m = cube.levels, cube.measures
>>> m["Continent"] = tt.switch(
...     l["City"],
...     {
...         frozenset({"Paris", "London"}): "Europe",
...         "Singapore": "Asia",
...         "NYC": "North America",
...     },
... )
>>> cube.query(m["Continent"], levels=[l["City"]])
               Continent
City
London            Europe
NYC        North America
Paris             Europe
Singapore           Asia
>>> m["Europe & Asia value"] = tt.agg.sum(
...     tt.switch(
...         m["Continent"],
...         {frozenset({"Europe", "Asia"}): m["Value.SUM"]},
...         default=0.0,
...     ),
...     scope=tt.OriginScope({l["Id"], l["City"]}),
... )
>>> cube.query(m["Europe & Asia value"], levels=[l["City"]])
          Europe & Asia value
City
London                   7.00
NYC                       .00
Paris                    8.00
Singapore                7.00
>>> cube.query(m["Europe & Asia value"])
  Europe & Asia value
0               22.00