[ENH]: Expand types allowed in Series.struct.field

This expands the set of types allowed by Series.struct.field to allow
those allowed by pyarrow.

pandas/core/arrays/arrow/accessors.py

@@ -13,6 +13,8 @@
     pa_version_under11p0,
 )
 
+from pandas.core.dtypes.common import is_list_like
+
 if not pa_version_under10p1:
     import pyarrow as pa
     import pyarrow.compute as pc
@@ -267,7 +269,16 @@ def dtypes(self) -> Series:
         names = [struct.name for struct in pa_type]
         return Series(types, index=Index(names))
 
-    def field(self, name_or_index: str | int) -> Series:
+    def field(
+        self,
+        name_or_index: list[str]
+        | list[bytes]
+        | list[int]
+        | pc.Expression
+        | bytes
+        | str
+        | int,
+    ) -> Series:
         """
         Extract a child field of a struct as a Series.
 
@@ -281,6 +292,17 @@ def field(self, name_or_index: str | int) -> Series:
         pandas.Series
             The data corresponding to the selected child field.
 
+        Notes
+        -----
+        The name of the resulting Series will be set using the following
+        rules:
+
+        - For string, bytes, or integer `name_or_index` (or a list of these, for
+          a nested selection), the Series name is set to the selected
+          field's name.
+        - For a :class:`pyarrow.compute.Expression`, this is set to
+          the string form of the expression.
+
         See Also
         --------
         Series.struct.explode : Return all child fields as a DataFrame.
@@ -314,27 +336,81 @@ def field(self, name_or_index: str | int) -> Series:
         1    2
         2    1
         Name: version, dtype: int64[pyarrow]
+
+        Or an expression
+
+        >>> import pyarrow.compute as pc
+        >>> s.struct.field(pc.field("project"))
+        0    pandas
+        1    pandas
+        2     numpy
+        Name: project, dtype: string[pyarrow]
+
+        For nested struct types, you can
+
+        >>> version_type = pa.struct([
+        ...     ("major", pa.int64()),
+        ...     ("minor", pa.int64()),
+        ... ])
+        >>> s = pd.Series(
+        ...     [
+        ...         {"version": {"major": 1, "minor": 5}, "project": "pandas"},
+        ...         {"version": {"major": 2, "minor": 1}, "project": "pandas"},
+        ...         {"version": {"major": 1, "minor": 26}, "project": "numpy"},
+        ...     ],
+        ...     dtype=pd.ArrowDtype(pa.struct(
+        ...         [("version", version_type), ("project", pa.string())]
+        ...     ))
+        ... )
+        >>> s.struct.field(["version", "minor"])
+        0     5
+        1     1
+        2    26
+        Name: minor, dtype: int64[pyarrow]
+        >>> s.struct.field([0, 0])
+        0    1
+        1    2
+        2    1
+        Name: major, dtype: int64[pyarrow]
         """
         from pandas import Series
 
+        def get_name(level_name_or_index, data):
+            if isinstance(level_name_or_index, int):
+                index = data.type.field(level_name_or_index).name
+            elif isinstance(level_name_or_index, (str, bytes)):
+                # index = pa_arr.type.get_field_index(level_name_or_index)
+                index = level_name_or_index
+            elif isinstance(level_name_or_index, pc.Expression):
+                index = str(level_name_or_index)
+            elif is_list_like(level_name_or_index):
+                # For nested input like [2, 1, 2]
+                # iteratively get the struct and field name. The last
+                # one is used for the name of the index.
+                level_name_or_index = list(reversed(level_name_or_index))
+                selected = data
+                while level_name_or_index:
+                    name_or_index = level_name_or_index.pop()
+                    name = get_name(name_or_index, selected)
+                    selected = selected.type.field(selected.type.get_field_index(name))
+                    index = selected.name
+                return index
+            else:
+                raise ValueError(
+                    "name_or_index must be an int, str, bytes, "
+                    "pyarrow.compute.Expression, or list of those"
+                )
+            return index
+
         pa_arr = self._data.array._pa_array
-        if isinstance(name_or_index, int):
-            index = name_or_index
-        elif isinstance(name_or_index, str):
-            index = pa_arr.type.get_field_index(name_or_index)
-        else:
-            raise ValueError(
-                "name_or_index must be an int or str, "
-                f"got {type(name_or_index).__name__}"
-            )
+        name = get_name(name_or_index, pa_arr)
+        field_arr = pc.struct_field(pa_arr, name_or_index)
 
-        pa_field = pa_arr.type[index]
-        field_arr = pc.struct_field(pa_arr, [index])
         return Series(
             field_arr,
             dtype=ArrowDtype(field_arr.type),
             index=self._data.index,
-            name=pa_field.name,
+            name=name,
         )
 
     def explode(self) -> DataFrame:

pandas/tests/series/accessors/test_struct_accessor.py

@@ -1,5 +1,6 @@
 import re
 
+import pyarrow.compute as pc
 import pytest
 
 from pandas import (
@@ -94,7 +95,7 @@ def test_struct_accessor_field():
 def test_struct_accessor_field_with_invalid_name_or_index():
     ser = Series([], dtype=ArrowDtype(pa.struct([("field", pa.int64())])))
 
-    with pytest.raises(ValueError, match="name_or_index must be an int or str"):
+    with pytest.raises(ValueError, match="name_or_index must be an int, str,"):
         ser.struct.field(1.1)
 
 
@@ -148,3 +149,40 @@ def test_struct_accessor_api_for_invalid(invalid):
         ),
     ):
         invalid.struct
+
+
+@pytest.mark.parametrize(
+    ["indices", "name"],
+    [
+        (0, "int_col"),
+        ([1, 2], "str_col"),
+        (pc.field("int_col"), "int_col"),
+        ("int_col", "int_col"),
+        (b"string_col", b"string_col"),
+        ([b"string_col"], "string_col"),
+    ],
+)
+def test_struct_accessor_field_expanded(indices, name):
+    arrow_type = pa.struct(
+        [
+            ("int_col", pa.int64()),
+            (
+                "struct_col",
+                pa.struct(
+                    [
+                        ("int_col", pa.int64()),
+                        ("float_col", pa.float64()),
+                        ("str_col", pa.string()),
+                    ]
+                ),
+            ),
+            (b"string_col", pa.string()),
+        ]
+    )
+
+    data = pa.array([], type=arrow_type)
+    ser = Series(data, dtype=ArrowDtype(arrow_type))
+    expected = pc.struct_field(data, indices)
+    result = ser.struct.field(indices)
+    tm.assert_equal(result.array._pa_array.combine_chunks(), expected)
+    assert result.name == name