>>> from datetime import date
>>> data = pd.DataFrame(
... {
... "Date": [date(2021, 1, 13), date(2021, 7, 5), date(2021, 7, 6)],
... "City": ["Paris", "Paris", "London"],
... "Age": [18, 25, 8],
... "Quantity": [200, 500, 100],
... }
... )
>>> table = session.read_pandas(
... data, default_values={"Age": 0}, table_name="City date table"
... )
>>> table.head()
Date City Age Quantity
0 2021-01-13 Paris 18 200
1 2021-07-05 Paris 25 500
2 2021-07-06 London 8 100
>>> cube = session.create_cube(table)
>>> h, l, m = cube.hierarchies, cube.levels, cube.measures
>>> h.update({name: {name: table[name]} for name in ["Date", "City", "Age"]})
>>> # Levels compared to constants of the same type:
>>> m["London Quantity.SUM"] = tt.filter(m["Quantity.SUM"], l["City"] == "London")
>>> m["Quantity.SUM before July"] = tt.filter(
... m["Quantity.SUM"], l["Date"] < date(2021, 7, 1)
... )
>>> m["Quantity.SUM for age under 18"] = tt.filter(
... m["Quantity.SUM"], l["Age"] <= 18
... )
>>> # A conjunction of conditions using the ``&`` operator:
>>> m["July Quantity.SUM in Paris"] = tt.filter(
... m["Quantity.SUM"],
... (
... (l["City"] == "Paris")
... & ((l["Date"]) >= date(2021, 7, 1))
... & (l["Date"] <= date(2021, 7, 31))
... ),
... )
>>> cube.query(
... m["Quantity.SUM"],
... m["London Quantity.SUM"],
... m["Quantity.SUM before July"],
... m["Quantity.SUM for age under 18"],
... m["July Quantity.SUM in Paris"],
... )
Quantity.SUM London Quantity.SUM Quantity.SUM before July Quantity.SUM for age under 18 July Quantity.SUM in Paris
0 800 100 200 300 500
>>> cube.query(
... m["Quantity.SUM"],
... m["London Quantity.SUM"],
... m["Quantity.SUM before July"],
... m["Quantity.SUM for age under 18"],
... m["July Quantity.SUM in Paris"],
... levels=[l["Date"], l["Age"], l["City"]],
... )
Quantity.SUM London Quantity.SUM Quantity.SUM before July Quantity.SUM for age under 18 July Quantity.SUM in Paris
Date Age City
2021-01-13 18 Paris 200 200 200
2021-07-05 25 Paris 500 500
2021-07-06 8 London 100 100 100
