>>> df = pd.DataFrame(
... columns=["Year", "Month", "Day", "Price"],
... data=[
... (2019, 7, 1, 15.0),
... (2019, 7, 2, 20.0),
... (2019, 6, 1, 25.0),
... (2019, 6, 2, 15.0),
... (2018, 7, 1, 5.0),
... (2018, 7, 2, 10.0),
... (2018, 6, 1, 15.0),
... (2018, 6, 2, 5.0),
... ],
... )
>>> table = session.read_pandas(
... df, default_values={"Year": 0, "Month": 0, "Day": 0}, table_name="Total"
... )
>>> cube = session.create_cube(table)
>>> h, l, m = cube.hierarchies, cube.levels, cube.measures
>>> h["Date"] = [table["Year"], table["Month"], table["Day"]]
>>> m["Total(Price)"] = tt.total(m["Price.SUM"], h["Date"])
>>> cube.query(
... m["Price.SUM"],
... m["Total(Price)"],
... levels=[l["Day"]],
... include_totals=True,
... )
Price.SUM Total(Price)
Year Month Day
Total 110.00 110.00
2018 35.00 110.00
6 20.00 110.00
1 15.00 110.00
2 5.00 110.00
7 15.00 110.00
1 5.00 110.00
2 10.00 110.00
2019 75.00 110.00
6 40.00 110.00
1 25.00 110.00
2 15.00 110.00
7 35.00 110.00
1 15.00 110.00
2 20.00 110.00
>>> h["Date"].slicing = True
>>> cube.query(
... m["Price.SUM"],
... m["Total(Price)"],
... levels=[l["Day"]],
... include_totals=True,
... )
Price.SUM Total(Price)
Year Month Day
2018 35.00 35.00
6 20.00 35.00
1 15.00 35.00
2 5.00 35.00
7 15.00 35.00
1 5.00 35.00
2 10.00 35.00
2019 75.00 75.00
6 40.00 75.00
1 25.00 75.00
2 15.00 75.00
7 35.00 75.00
1 15.00 75.00
2 20.00 75.00
