Add test for user-defined aggregation function (UDAF) with DataFusion

- Implement MyAccumulator class following Accumulator interface
- Register UDAF named "my_accumulator" in SessionContext
- Create test DataFrame and run SQL query using UDAF with GROUP BY
- Verify results match expected aggregated values
- Ensure correct integration and functionality of UDAF in Python bindings

Author: kosiew

test_udaf_script.py

@@ -0,0 +1,74 @@
+import pyarrow as pa
+import pyarrow.compute as pc
+from datafusion import Accumulator, SessionContext, udaf
+
+
+# Define a user-defined aggregation function (UDAF)
+class MyAccumulator(Accumulator):
+    """
+    Interface of a user-defined accumulation.
+    """
+
+    def __init__(self) -> None:
+        self._sum = pa.scalar(0.0)
+
+    def update(self, values: pa.Array) -> None:
+        # Not nice since pyarrow scalars can't be summed yet. This breaks on `None`
+        self._sum = pa.scalar(self._sum.as_py() + pc.sum(values).as_py())
+
+    def merge(self, states: list[pa.Array]) -> None:
+        # Not nice since pyarrow scalars can't be summed yet. This breaks on `None`
+        self._sum = pa.scalar(self._sum.as_py() + pc.sum(states[0]).as_py())
+
+    def state(self) -> list[pa.Scalar]:
+        return [self._sum]
+    
+    def evaluate(self) -> pa.Scalar:
+        return self._sum
+
+
+my_udaf = udaf(
+    MyAccumulator,
+    pa.float64(),
+    pa.float64(),
+    [pa.float64()],
+    "stable",
+    # This will be the name of the UDAF in SQL
+    # If not specified it will by default the same as accumulator class name
+    name="my_accumulator",
+)
+
+# Create a context
+ctx = SessionContext()
+
+# Create a datafusion DataFrame from a Python dictionary
+source_df = ctx.from_pydict({"a": [1, 1, 3], "b": [4, 5, 6]}, name="t")
+# Dataframe:
+# +---+---+
+# | a | b |
+# +---+---+
+# | 1 | 4 |
+# | 1 | 5 |
+# | 3 | 6 |
+# +---+---+
+
+# Register UDF for use in SQL
+ctx.register_udaf(my_udaf)
+
+# Query the DataFrame using SQL
+result_df = ctx.sql(
+    "select a, my_accumulator(b) as b_aggregated from t group by a order by a"
+)
+# Dataframe:
+# +---+--------------+
+# | a | b_aggregated |
+# +---+--------------+
+# | 1 | 9            |
+# | 3 | 6            |
+# +---+--------------+
+
+result_dict = result_df.to_pydict()
+print("Result:", result_dict)
+assert result_dict["a"] == [1, 3]
+assert result_dict["b_aggregated"] == [9.0, 6.0]
+print("Test passed successfully!")