Level.isin()

>>> df = pd.DataFrame(
...     columns=["City", "Price"],
...     data=[
...         ("Berlin", 150.0),
...         ("London", 240.0),
...         ("New York", 270.0),
...         ("Paris", 200.0),
...     ],
... )
>>> table = session.read_pandas(df, keys={"City"}, table_name="Example")
>>> cube = session.create_cube(table)
>>> l, m = cube.levels, cube.measures
>>> m["Price.SUM in London and Paris"] = tt.filter(
...     m["Price.SUM"], l["City"].isin("London", "Paris")
... )
>>> cube.query(
...     m["Price.SUM"],
...     m["Price.SUM in London and Paris"],
...     levels=[l["City"]],
... )
         Price.SUM Price.SUM in London and Paris
City
Berlin      150.00
London      240.00                        240.00
New York    270.00
Paris       200.00                        200.00