Method test_groupby_monotonic

pandas/tests/window/test_groupby.py:975–1005

(self)

            df.groupby("c").rolling(on="t", window="3s")

    def test_groupby_monotonic(self):
        # GH 15130
        # we don't need to validate monotonicity when grouping

        # GH 43909 we should raise an error here to match
        # behaviour of non-groupby rolling.

        data = [
            ["David", "1/1/2015", 100],
            ["David", "1/5/2015", 500],
            ["David", "5/30/2015", 50],
            ["David", "7/25/2015", 50],
            ["Ryan", "1/4/2014", 100],
            ["Ryan", "1/19/2015", 500],
            ["Ryan", "3/31/2016", 50],
            ["Joe", "7/1/2015", 100],
            ["Joe", "9/9/2015", 500],
            ["Joe", "10/15/2015", 50],
        ]

        df = DataFrame(data=data, columns=["name", "date", "amount"])
        df["date"] = to_datetime(df["date"])
        df = df.sort_values("date")

        expected = (
            df.set_index("date")
            .groupby("name")
            .apply(lambda x: x.rolling("180D")["amount"].sum())
        )
        result = df.groupby("name").rolling("180D", on="date")["amount"].sum()
        tm.assert_series_equal(result, expected)

    def test_datelike_on_monotonic_within_each_group(self):
        # GH 13966 (similar to #15130, closed by #15175)