docs: Example of calling Python UDF & UDAF in SQL

README.md

@@ -95,7 +95,11 @@ See [examples](examples/README.md) for more information.
 - [Query a Parquet file using SQL](./examples/sql-parquet.py)
 - [Query a Parquet file using the DataFrame API](./examples/dataframe-parquet.py)
 - [Run a SQL query and store the results in a Pandas DataFrame](./examples/sql-to-pandas.py)
+- [Run a SQL query with a Python user-defined function (UDF)](./examples/sql-using-python-udf.py)
+- [Run a SQL query with a Python user-defined aggregation function (UDAF)](./examples/sql-using-python-udaf.py)
 - [Query PyArrow Data](./examples/query-pyarrow-data.py)
+- [Create dataframe](./examples/import.py)
+- [Export dataframe](./examples/export.py)
 
 ### Running User-Defined Python Code
 

datafusion/__init__.py

@@ -171,7 +171,7 @@ def udf(func, input_types, return_type, volatility, name=None):
     if not callable(func):
         raise TypeError("`func` argument must be callable")
     if name is None:
-        name = func.__qualname__
+        name = func.__qualname__.lower()
     return ScalarUDF(
         name=name,
         func=func,
@@ -190,7 +190,7 @@ def udaf(accum, input_type, return_type, state_type, volatility, name=None):
             "`accum` must implement the abstract base class Accumulator"
         )
     if name is None:
-        name = accum.__qualname__
+        name = accum.__qualname__.lower()
     return AggregateUDF(
         name=name,
         accumulator=accum,

examples/sql-using-python-udaf.py

@@ -0,0 +1,91 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+from datafusion import udaf, SessionContext, Accumulator
+import pyarrow as pa
+
+
+# Define a user-defined aggregation function (UDAF)
+class MyAccumulator(Accumulator):
+    """
+    Interface of a user-defined accumulation.
+    """
+
+    def __init__(self):
+        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() + pa.compute.sum(values).as_py()
+        )
+
+    def merge(self, states: 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() + pa.compute.sum(states).as_py()
+        )
+
+    def state(self) -> pa.Array:
+        return pa.array([self._sum.as_py()])
+
+    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]})
+# 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
+table_name = ctx.catalog().database().names().pop()
+result_df = ctx.sql(
+    f"select a, my_accumulator(b) as b_aggregated from {table_name} group by a order by a"
+)
+# Dataframe:
+# +---+--------------+
+# | a | b_aggregated |
+# +---+--------------+
+# | 1 | 9            |
+# | 3 | 6            |
+# +---+--------------+
+assert result_df.to_pydict()["a"] == [1, 3]
+assert result_df.to_pydict()["b_aggregated"] == [9, 6]

examples/sql-using-python-udf.py

@@ -0,0 +1,65 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+from datafusion import udf, SessionContext
+import pyarrow as pa
+
+
+# Define a user-defined function (UDF)
+def is_null(array: pa.Array) -> pa.Array:
+    return array.is_null()
+
+
+is_null_arr = udf(
+    is_null,
+    [pa.int64()],
+    pa.bool_(),
+    "stable",
+    # This will be the name of the UDF in SQL
+    # If not specified it will by default the same as Python function name
+    name="is_null",
+)
+
+# Create a context
+ctx = SessionContext()
+
+# Create a datafusion DataFrame from a Python dictionary
+source_df = ctx.from_pydict({"a": [1, 2, 3], "b": [4, None, 6]})
+# Dataframe:
+# +---+---+
+# | a | b |
+# +---+---+
+# | 1 | 4 |
+# | 2 |   |
+# | 3 | 6 |
+# +---+---+
+
+# Register UDF for use in SQL
+ctx.register_udf(is_null_arr)
+
+# Query the DataFrame using SQL
+table_name = ctx.catalog().database().names().pop()
+result_df = ctx.sql(f"select a, is_null(b) as b_is_null from {table_name}")
+# Dataframe:
+# +---+-----------+
+# | a | b_is_null |
+# +---+-----------+
+# | 1 | false     |
+# | 2 | true      |
+# | 3 | false     |
+# +---+-----------+
+assert result_df.to_pydict()["b_is_null"] == [False, True, False]