Handle case of scan aggregation in groupby-transform

python/cudf/cudf/core/groupby/groupby.py

@@ -1767,13 +1767,23 @@ def transform(self, function):
         --------
         agg
         """
+        if not (isinstance(function, str) or callable(function)):
+            raise ValueError(
+                "Aggregation must be a named aggregation or a callable"
+            )
         try:
             result = self.agg(function)
         except TypeError as e:
             raise NotImplementedError(
                 "Currently, `transform()` supports only aggregations."
             ) from e
-
+        # If the aggregation is a scan, don't broadcast
+        if libgroupby._is_all_scan_aggregate([[function]]):
+            if len(result) != len(self.obj):
+                raise AssertionError(
+                    "Unexpected result length for scan transform"
+                )
+            return result
         return self._broadcast(result)
 
     def rolling(self, *args, **kwargs):

python/cudf/cudf/tests/groupby/test_transform.py

@@ -0,0 +1,43 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+import itertools
+
+import pytest
+
+import cudf
+from cudf.testing._utils import assert_eq
+
+
+@pytest.fixture(params=[False, True], ids=["no-null-keys", "null-keys"])
+def keys_null(request):
+    return request.param
+
+
+@pytest.fixture(params=[False, True], ids=["no-null-values", "null-values"])
+def values_null(request):
+    return request.param
+
+
+@pytest.fixture
+def df(keys_null, values_null):
+    keys = ["a", "b", "a", "c", "b", "b", "c", "a"]
+    r = range(len(keys))
+    if keys_null:
+        keys[::3] = itertools.repeat(None, len(r[::3]))
+    values = list(range(len(keys)))
+    if values_null:
+        values[1::3] = itertools.repeat(None, len(r[1::3]))
+    return cudf.DataFrame({"key": keys, "values": values})
+
+
+@pytest.mark.parametrize("agg", ["cumsum", "cumprod", "max", "sum", "prod"])
+def test_transform_broadcast(agg, df):
+    pf = df.to_pandas()
+    got = df.groupby("key").transform(agg)
+    expect = pf.groupby("key").transform(agg)
+    assert_eq(got, expect, check_dtype=False)
+
+
+def test_transform_invalid():
+    df = cudf.DataFrame({"key": [1, 1], "values": [4, 5]})
+    with pytest.raises(ValueError):
+        df.groupby("key").transform({"values": "cumprod"})