>>> df = pd.DataFrame(
... columns=["City", "A", "B", "C", "D"],
... data=[
... ("Berlin", 15.0, 10.0, 10.1, 1.0),
... ("London", 24.0, 16.0, 20.5, 3.14),
... ("New York", -27.0, 15.0, 30.7, 10.0),
... ("Paris", 0.0, 0.0, 0.0, 0.0),
... ],
... )
>>> table = session.read_pandas(df, keys={"City"}, table_name="Math")
>>> cube = session.create_cube(table)
>>> l, m = cube.levels, cube.measures
>>> m["Float division"] = m["A.SUM"] / m["B.SUM"]
>>> m["Float division is NaN"] = tt.math.isnan(m["Float division"])
>>> m["String is NaN"] = tt.math.isnan(tt.agg.single_value(table["City"]))
>>> cube.query(
... m["Float division"],
... m["Float division is NaN"],
... m["String is NaN"],
... levels=[l["City"]],
... )
Float division Float division is NaN String is NaN
City
Berlin 1.50 False False
London 1.50 False False
New York -1.80 False False
Paris NaN True False
