fix(python): improve support for user-defined functions that return scalars

crates/polars-plan/src/dsl/python_udf.rs

@@ -104,14 +104,21 @@ pub struct PythonUdfExpression {
     python_function: PyObject,
     output_type: Option<DataType>,
     is_elementwise: bool,
+    returns_scalar: bool,
 }
 
 impl PythonUdfExpression {
-    pub fn new(lambda: PyObject, output_type: Option<DataType>, is_elementwise: bool) -> Self {
+    pub fn new(
+        lambda: PyObject,
+        output_type: Option<DataType>,
+        is_elementwise: bool,
+        returns_scalar: bool,
+    ) -> Self {
         Self {
             python_function: lambda,
             output_type,
             is_elementwise,
+            returns_scalar,
         }
     }
 
@@ -121,7 +128,7 @@ impl PythonUdfExpression {
         // skip header
         let buf = &buf[MAGIC_BYTE_MARK.len()..];
         let mut reader = Cursor::new(buf);
-        let (output_type, is_elementwise): (Option<DataType>, bool) =
+        let (output_type, is_elementwise, returns_scalar): (Option<DataType>, bool, bool) =
             ciborium::de::from_reader(&mut reader).map_err(map_err)?;
 
         let remainder = &buf[reader.position() as usize..];
@@ -138,6 +145,7 @@ impl PythonUdfExpression {
                 python_function.into(),
                 output_type,
                 is_elementwise,
+                returns_scalar,
             )) as Arc<dyn SeriesUdf>)
         })
     }
@@ -181,8 +189,15 @@ impl SeriesUdf for PythonUdfExpression {
     #[cfg(feature = "serde")]
     fn try_serialize(&self, buf: &mut Vec<u8>) -> PolarsResult<()> {
         buf.extend_from_slice(MAGIC_BYTE_MARK);
-        ciborium::ser::into_writer(&(self.output_type.clone(), self.is_elementwise), &mut *buf)
-            .unwrap();
+        ciborium::ser::into_writer(
+            &(
+                self.output_type.clone(),
+                self.is_elementwise,
+                self.returns_scalar,
+            ),
+            &mut *buf,
+        )
+        .unwrap();
 
         Python::with_gil(|py| {
             let pickle = PyModule::import_bound(py, "cloudpickle")
@@ -222,6 +237,7 @@ impl Expr {
             (ApplyOptions::GroupWise, "python_udf")
         };
 
+        let returns_scalar = func.returns_scalar;
         let return_dtype = func.output_type.clone();
         let output_type = GetOutput::map_field(move |fld| match return_dtype {
             Some(ref dt) => Field::new(fld.name(), dt.clone()),
@@ -239,6 +255,7 @@ impl Expr {
             options: FunctionOptions {
                 collect_groups,
                 fmt_str: name,
+                returns_scalar,
                 ..Default::default()
             },
         }

py-polars/polars/expr/expr.py

@@ -4524,21 +4524,22 @@ def map_batches(
         *,
         agg_list: bool = False,
         is_elementwise: bool = False,
+        returns_scalar: bool = False,
     ) -> Self:
         """
         Apply a custom python function to a whole Series or sequence of Series.
 
-        The output of this custom function must be a Series (or a NumPy array, in which
-        case it will be automatically converted into a Series). If you want to apply a
+        The output of this custom function is presumed to be either a Series,
+        or a NumPy array (in which case it will be automatically converted into
+        a Series), or a scalar that will be converted into a Series. If the
+        result is a scalar and you want it to stay as a scalar, pass in
+        ``returns_scalar=True``. If you want to apply a
         custom function elementwise over single values, see :func:`map_elements`.
         A reasonable use case for `map` functions is transforming the values
         represented by an expression using a third-party library.
 
-        .. warning::
-            If you are looking to map a function over a window function or group_by
-            context, refer to :func:`map_elements` instead.
-            Read more in `the book
-            <https://docs.pola.rs/user-guide/expressions/user-defined-functions>`_.
+        If your function returns a scalar, for example a float, use
+        :func:`map_to_scalar` instead.
 
         Parameters
         ----------
@@ -4556,6 +4557,11 @@ def map_batches(
             function. This parameter only works in a group-by context.
             The function will be invoked only once on a list of groups, rather than
             once per group.
+        returns_scalar
+            If the function returns a scalar, by default it will be wrapped in
+            a list in the output, since the assumption is that the function
+            always returns something Series-like. If you want to keep the
+            result as a scalar, set this argument to True.
 
         Warnings
         --------
@@ -4597,33 +4603,58 @@ def map_batches(
         ...     }
         ... )
         >>> df.group_by("a").agg(
-        ...     pl.col("b").map_batches(lambda x: x.max(), agg_list=False)
+        ...     pl.col("b").map_batches(lambda x: x + 2, agg_list=False)
         ... )  # doctest: +IGNORE_RESULT
         shape: (2, 2)
         ┌─────┬───────────┐
         │ a   ┆ b         │
         │ --- ┆ ---       │
         │ i64 ┆ list[i64] │
         ╞═════╪═══════════╡
-        │ 1   ┆ [4]       │
-        │ 0   ┆ [3]       │
+        │ 1   ┆ [4, 6]    │
+        │ 0   ┆ [3, 5]    │
         └─────┴───────────┘
 
         Using `agg_list=True` would be more efficient. In this example, the input of
         the function is a Series of type `List(Int64)`.
 
         >>> df.group_by("a").agg(
-        ...     pl.col("b").map_batches(lambda x: x.list.max(), agg_list=True)
+        ...     pl.col("b").map_batches(
+        ...         lambda x: x.list.eval(pl.element() + 2), agg_list=True
+        ...     )
+        ... )  # doctest: +IGNORE_RESULT
+        shape: (2, 2)
+        ┌─────┬───────────┐
+        │ a   ┆ b         │
+        │ --- ┆ ---       │
+        │ i64 ┆ list[i64] │
+        ╞═════╪═══════════╡
+        │ 0   ┆ [3, 5]    │
+        │ 1   ┆ [4, 6]    │
+        └─────┴───────────┘
+
+        Here's an example of a function that returns a scalar, where we want it
+        to stay as a scalar:
+
+        >>> df = pl.DataFrame(
+        ...     {
+        ...         "a": [0, 1, 0, 1],
+        ...         "b": [1, 2, 3, 4],
+        ...     }
+        ... )
+        >>> df.group_by("a").agg(
+        ...     pl.col("b").map_batches(lambda x: x.max(), returns_scalar=True)
         ... )  # doctest: +IGNORE_RESULT
         shape: (2, 2)
         ┌─────┬─────┐
         │ a   ┆ b   │
         │ --- ┆ --- │
         │ i64 ┆ i64 │
         ╞═════╪═════╡
-        │ 0   ┆ 3   │
         │ 1   ┆ 4   │
+        │ 0   ┆ 3   │
         └─────┴─────┘
+
         """
         if return_dtype is not None:
             return_dtype = py_type_to_dtype(return_dtype)
@@ -4634,6 +4665,7 @@ def map_batches(
                 return_dtype,
                 agg_list,
                 is_elementwise,
+                returns_scalar,
             )
         )
 

py-polars/polars/series/series.py

@@ -1380,6 +1380,7 @@ def __array_ufunc__(
 
             # Only generalized ufuncs have a signature set:
             is_generalized_ufunc = bool(ufunc.signature)
+
             if is_generalized_ufunc:
                 # Generalized ufuncs will operate on the whole array, so
                 # missing data can corrupt the results.
@@ -1392,7 +1393,13 @@ def __array_ufunc__(
                 # output size.
                 assert ufunc.signature is not None  # pacify MyPy
                 ufunc_input, ufunc_output = ufunc.signature.split("->")
-                allocate_output = ufunc_input == ufunc_output
+                if ufunc_output == "()":
+                    # If the result a scalar, just let the function do its
+                    # thing, no need for any song and dance involving
+                    # allocation:
+                    return ufunc(*args, dtype=dtype_char, **kwargs)
+                else:
+                    allocate_output = ufunc_input == ufunc_output
             else:
                 allocate_output = True
 
@@ -1409,6 +1416,7 @@ def __array_ufunc__(
                 lambda out: ufunc(*args, out=out, dtype=dtype_char, **kwargs),
                 allocate_output,
             )
+
             result = self._from_pyseries(series)
             if is_generalized_ufunc:
                 # In this case we've disallowed passing in missing data, so no
@@ -1426,7 +1434,6 @@ def __array_ufunc__(
                 .select(F.when(validity_mask).then(F.col(self.name)))
                 .to_series(0)
             )
-
         else:
             msg = (
                 "only `__call__` is implemented for numpy ufuncs on a Series, got "

py-polars/src/expr/general.rs

@@ -755,15 +755,23 @@ impl PyExpr {
         self.inner.clone().shrink_dtype().into()
     }
 
-    #[pyo3(signature = (lambda, output_type, agg_list, is_elementwise))]
+    #[pyo3(signature = (lambda, output_type, agg_list, is_elementwise, returns_scalar))]
     fn map_batches(
         &self,
         lambda: PyObject,
         output_type: Option<Wrap<DataType>>,
         agg_list: bool,
         is_elementwise: bool,
+        returns_scalar: bool,
     ) -> Self {
-        map_single(self, lambda, output_type, agg_list, is_elementwise)
+        map_single(
+            self,
+            lambda,
+            output_type,
+            agg_list,
+            is_elementwise,
+            returns_scalar,
+        )
     }
 
     fn dot(&self, other: Self) -> Self {

py-polars/src/map/lazy.rs

@@ -129,10 +129,12 @@ pub fn map_single(
     output_type: Option<Wrap<DataType>>,
     agg_list: bool,
     is_elementwise: bool,
+    returns_scalar: bool,
 ) -> PyExpr {
     let output_type = output_type.map(|wrap| wrap.0);
 
-    let func = python_udf::PythonUdfExpression::new(lambda, output_type, is_elementwise);
+    let func =
+        python_udf::PythonUdfExpression::new(lambda, output_type, is_elementwise, returns_scalar);
     pyexpr.inner.clone().map_python(func, agg_list).into()
 }
 

py-polars/tests/unit/interop/numpy/test_ufunc_expr.py

@@ -133,6 +133,44 @@ def test_grouped_ufunc() -> None:
     df.group_by("id").agg(pl.col("values").log1p().sum().pipe(np.expm1))
 
 
+def test_generalized_ufunc_scalar() -> None:
+    numba = pytest.importorskip("numba")
+
+    @numba.guvectorize([(numba.int64[:], numba.int64[:])], "(n)->()")  # type: ignore[misc]
+    def my_custom_sum(arr, result) -> None:  # type: ignore[no-untyped-def]
+        total = 0
+        for value in arr:
+            total += value
+        result[0] = total
+
+    # Make type checkers happy:
+    custom_sum = cast(Callable[[object], object], my_custom_sum)
+
+    # Demonstrate NumPy as the canonical expected behavior:
+    assert custom_sum(np.array([10, 2, 3], dtype=np.int64)) == 15
+
+    # Direct call of the gufunc:
+    df = pl.DataFrame({"values": [10, 2, 3]})
+    assert custom_sum(df.get_column("values")) == 15
+
+    # Indirect call of the gufunc:
+    indirect = df.select(pl.col("values").map_batches(custom_sum, returns_scalar=True))
+    assert_frame_equal(indirect, pl.DataFrame({"values": 15}))
+    indirect = df.select(pl.col("values").map_batches(custom_sum, returns_scalar=False))
+    assert_frame_equal(indirect, pl.DataFrame({"values": [15]}))
+
+    # group_by()
+    df = pl.DataFrame({"labels": ["a", "b", "a", "b"], "values": [10, 2, 3, 30]})
+    indirect = (
+        df.group_by("labels")
+        .agg(pl.col("values").map_batches(custom_sum, returns_scalar=True))
+        .sort("labels")
+    )
+    assert_frame_equal(
+        indirect, pl.DataFrame({"labels": ["a", "b"], "values": [13, 32]})
+    )
+
+
 def make_gufunc_mean() -> Callable[[pl.Series], pl.Series]:
     numba = pytest.importorskip("numba")
 

py-polars/tests/unit/operations/map/test_map_batches.py

@@ -68,6 +68,10 @@ def test_map_batches_group() -> None:
     assert df.group_by("id").agg(pl.col("t").map_batches(lambda s: s.sum())).sort(
         "id"
     ).to_dict(as_series=False) == {"id": [0, 1], "t": [[11], [35]]}
+    # If returns_scalar is True, the result won't be wrapped in a list:
+    assert df.group_by("id").agg(
+        pl.col("t").map_batches(lambda s: s.sum(), returns_scalar=True)
+    ).sort("id").to_dict(as_series=False) == {"id": [0, 1], "t": [11, 35]}
 
 
 def test_map_deprecated() -> None:
@@ -82,16 +86,10 @@ def test_map_deprecated() -> None:
 def test_ufunc_args() -> None:
     df = pl.DataFrame({"a": [1, 2, 3], "b": [2, 4, 6]})
     result = df.select(
-        z=np.add(  # type: ignore[call-overload]
-            pl.col("a"), pl.col("b")
-        )
+        z=np.add(pl.col("a"), pl.col("b"))  # type: ignore[call-overload]
     )
     expected = pl.DataFrame({"z": [3, 6, 9]})
     assert_frame_equal(result, expected)
-    result = df.select(
-        z=np.add(  # type: ignore[call-overload]
-            2, pl.col("a")
-        )
-    )
+    result = df.select(z=np.add(2, pl.col("a")))  # type: ignore[call-overload]
     expected = pl.DataFrame({"z": [3, 4, 5]})
     assert_frame_equal(result, expected)