>>> df = pd.DataFrame(
... columns=["Year", "Month", "Day", "Quantity"],
... data=[
... (2000, 1, 1, 15),
... (2000, 1, 2, 10),
... (2000, 2, 1, 30),
... (2000, 2, 2, 20),
... (2000, 2, 5, 30),
... (2000, 4, 4, 5),
... (2000, 4, 5, 10),
... (2020, 12, 6, 15),
... (2020, 12, 7, 15),
... ],
... )
>>> table = session.read_pandas(
... df, table_name="Rank", default_values={"Year": 0, "Month": 0, "Day": 0}
... )
>>> cube = session.create_cube(table)
>>> h, l, m = cube.hierarchies, cube.levels, cube.measures
>>> h["Date"] = [table["Year"], table["Month"], table["Day"]]
>>> m["Rank"] = tt.rank(m["Quantity.SUM"], h["Date"])
>>> cube.query(
... m["Quantity.SUM"],
... m["Rank"],
... levels=[l["Day"]],
... include_totals=True,
... )
Quantity.SUM Rank
Year Month Day
Total 150 1
2000 120 2
1 25 2
1 15 2
2 10 1
2 80 3
1 30 2
2 20 1
5 30 3
4 15 1
4 5 1
5 10 2
2020 30 1
12 30 1
6 15 1
7 15 2
>>> m["Rank with filters not applied"] = tt.rank(
... m["Quantity.SUM"], h["Date"], apply_filters=False
... )
>>> cube.query(
... m["Quantity.SUM"],
... m["Rank"],
... m["Rank with filters not applied"],
... levels=[l["Month"]],
... include_totals=True,
... filter=l["Year"] == "2000",
... )
Quantity.SUM Rank Rank with filters not applied
Year Month
Total 120 1 1
2000 120 1 2
1 25 2 2
2 80 3 3
4 15 1 1
