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fix: rolling_* aggs were behaving as if they return scalars in group-by #15657

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merged 1 commit into from
Apr 15, 2024
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fix: rolling_* aggs were behaving as if they return scalars in group-by #15657

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2 changes: 1 addition & 1 deletion crates/polars-plan/src/dsl/mod.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -1208,7 +1208,7 @@ impl Expr {
self.apply_many_private(
FunctionExpr::RollingExpr(rolling_function_by(options)),
&[col(&name)],
true,
false,
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Ai, good catch. That took a long time.

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@reswqa reswqa Apr 15, 2024 •
edited
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Just wondering what's the principle here, it seems to be the same behavior for reduce/fold (return_scalars=True).

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Rolling functions return something which is the same length as the input, so I don't think they should ever return a scalar

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Yeap, I see. But IIRC, the output of fold/reduce should also be consistent with the length of the input.

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ah thanks - will take a look, have made a note-to-self

false,
)
} else {
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13 changes: 13 additions & 0 deletions py-polars/tests/unit/operations/rolling/test_rolling.py
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Expand Up @@ -917,3 +917,16 @@ def test_rolling_min_periods(
)
)["value"]
assert_series_equal(result, pl.Series("value", expected, pl.Int64))


def test_rolling_returns_scalar_15656() -> None:
df = pl.DataFrame(
{
"a": [date(2020, 1, 1), date(2020, 1, 2), date(2020, 1, 3)],
"b": [4, 5, 6],
"c": [1, 2, 3],
}
)
result = df.group_by("c").agg(pl.col("b").rolling_mean("2d", by="a")).sort("c")
expected = pl.DataFrame({"c": [1, 2, 3], "b": [[4.0], [5.0], [6.0]]})
assert_frame_equal(result, expected)
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