fix: Null handling in full-null group_by_dynamic mean/sum (#23435)

Author: orlp

crates/polars-compute/src/rolling/nulls/mean.rs

@@ -32,7 +32,12 @@ impl<
 
     unsafe fn update(&mut self, start: usize, end: usize) -> Option<T> {
         let sum = self.sum.update(start, end);
-        sum.map(|sum| sum / NumCast::from(end - start - self.sum.null_count).unwrap())
+        let len = end - start;
+        if self.sum.null_count == len {
+            None
+        } else {
+            sum.map(|sum| sum / NumCast::from(end - start - self.sum.null_count).unwrap())
+        }
     }
     fn is_valid(&self, min_periods: usize) -> bool {
         self.sum.is_valid(min_periods)

crates/polars-compute/src/rolling/nulls/sum.rs

@@ -155,7 +155,7 @@ where
             }
         }
         self.last_end = end;
-        self.sum.and_then(NumCast::from)
+        self.sum.and_then(NumCast::from).or(Some(T::zeroed()))
     }
 
     fn is_valid(&self, min_periods: usize) -> bool {

py-polars/tests/unit/operations/test_group_by_dynamic.py

@@ -1082,3 +1082,52 @@ def test_group_by_dynamic_single_row_22585() -> None:
         {"group": ["x"], "date": [date(2025, 1, 1)], "len": [1]}
     ).with_columns(pl.col("len").cast(pl.UInt32))
     assert_frame_equal(expected, out)
+
+
+def test_group_by_dynamic_zero_sum_23433() -> None:
+    df = pl.DataFrame(
+        {
+            "g": [0, 0, 1, 1, 2, 2, 2, 3, 3],
+            "x": [None, None, None, None, None, None, None, 1, 2],
+        }
+    )
+    out = df.group_by_dynamic("g", every="1i", period="2i").agg(pl.col.x.sum())
+    expected = pl.DataFrame({"g": [0, 1, 2, 3], "x": [0, 0, 3, 3]})
+    assert_frame_equal(out, expected)
+
+
+def test_group_by_dynamic_null_mean_22724() -> None:
+    time = pl.datetime_range(
+        start=datetime(2025, 1, 1, 0, 0, 00),
+        end=datetime(2025, 1, 1, 0, 0, 10),
+        interval="1s",
+        eager=True,
+    )
+
+    b = pl.DataFrame(
+        {
+            "time": time,
+            "value": [None, None, None, 0, None, None, None, None, -1, None, None],
+            "empty": [None] * len(time),
+        }
+    ).cast({"value": pl.Float32, "empty": pl.Float32})
+    gb = b.group_by_dynamic("time", every="2s", period="3s", offset="-3s")
+    out = gb.agg([pl.col("value").cast(pl.Float32).mean()])
+
+    expected = pl.DataFrame(
+        {
+            "time": pl.Series(
+                [
+                    datetime(2024, 12, 31, 23, 59, 59),
+                    datetime(2025, 1, 1, 0, 0, 1),
+                    datetime(2025, 1, 1, 0, 0, 3),
+                    datetime(2025, 1, 1, 0, 0, 5),
+                    datetime(2025, 1, 1, 0, 0, 7),
+                    datetime(2025, 1, 1, 0, 0, 9),
+                ],
+                dtype=pl.Datetime(time_unit="us", time_zone=None),
+            ),
+            "value": pl.Series([None, 0.0, 0.0, None, -1.0, None], dtype=pl.Float32),
+        }
+    )
+    assert_frame_equal(out, expected)