import pandas as pd

orders = pd.DataFrame(
    {
        "date": pd.to_datetime(
            [
                "2026-01-01",
                "2026-01-02",
                "2026-01-03",
                "2026-01-04",
                "2026-01-05",
                "2026-01-06",
            ]
        ),
        "orders": [18, 22, 17, 25, 29, 24],
        "sales_usd": [420.0, 510.0, 460.0, 590.0, 640.0, 610.0],
    }
)

orders = orders.sort_values("date").set_index("date")

result = orders.assign(
    sales_mean_3row=orders["sales_usd"]
    .rolling(window=3, min_periods=2)
    .mean()
    .round(2),
    sales_total_3row=orders["sales_usd"].rolling(window=3, min_periods=2).sum(),
)

print("fixed-row rolling window")
print(result.to_string())
print()

check_date = pd.Timestamp("2026-01-05")
window_values = orders.loc["2026-01-03":"2026-01-05", "sales_usd"]

print("window values for 2026-01-05:", window_values.tolist())
print("verified average:", result.loc[check_date, "sales_mean_3row"])
print("verified total:", result.loc[check_date, "sales_total_3row"])

assert result.loc[check_date, "sales_mean_3row"] == round(window_values.mean(), 2)
assert result.loc[check_date, "sales_total_3row"] == window_values.sum()

irregular = pd.DataFrame(
    {
        "date": pd.to_datetime(
            ["2026-01-01", "2026-01-02", "2026-01-05", "2026-01-06"]
        ),
        "sales_usd": [420.0, 510.0, 640.0, 610.0],
    }
)

time_window = irregular.assign(
    sales_total_3d=irregular.rolling(
        window="3D", on="date", min_periods=1
    )["sales_usd"].sum()
)

print()
print("time-offset rolling window")
print(time_window.to_string(index=False))
print()
print("verification: rolling windows match source rows")