BUG: numba raises for string columns or index

doc/source/whatsnew/v2.2.0.rst

@@ -496,8 +496,8 @@ Conversion
 Strings
 ^^^^^^^
 - Bug in :func:`pandas.api.types.is_string_dtype` while checking object array with no elements is of the string dtype (:issue:`54661`)
+- Bug in :meth:`DataFrame.apply` failing when ``engine="numba"`` and columns or index have ``StringDtype`` (:issue:`56189`)
 - Bug in :meth:`Series.str.startswith` and :meth:`Series.str.endswith` with arguments of type ``tuple[str, ...]`` for ``string[pyarrow]`` (:issue:`54942`)
--
 
 Interval
 ^^^^^^^^

pandas/core/apply.py

@@ -1172,11 +1172,17 @@ def apply_with_numba(self) -> dict[int, Any]:
         )
         from pandas.core._numba.extensions import set_numba_data
 
+        index = self.obj.index
+        if index.dtype == "string":
+            index = index.astype(object)
+
+        columns = self.obj.columns
+        if columns.dtype == "string":
+            columns = columns.astype(object)
+
         # Convert from numba dict to regular dict
         # Our isinstance checks in the df constructor don't pass for numbas typed dict
-        with set_numba_data(self.obj.index) as index, set_numba_data(
-            self.columns
-        ) as columns:
+        with set_numba_data(index) as index, set_numba_data(columns) as columns:
             res = dict(nb_func(self.values, columns, index))
         return res
 

pandas/tests/apply/test_numba.py

@@ -24,6 +24,22 @@ def test_numba_vs_python_noop(float_frame, apply_axis):
     tm.assert_frame_equal(result, expected)
 
 
+def test_numba_vs_python_string_index():
+    # GH#56189
+    pytest.importorskip("pyarrow")
+    df = DataFrame(
+        1,
+        index=Index(["a", "b"], dtype="string[pyarrow_numpy]"),
+        columns=Index(["x", "y"], dtype="string[pyarrow_numpy]"),
+    )
+    func = lambda x: x
+    result = df.apply(func, engine="numba", axis=0)
+    expected = df.apply(func, engine="python", axis=0)
+    tm.assert_frame_equal(
+        result, expected, check_column_type=False, check_index_type=False
+    )
+
+
 def test_numba_vs_python_indexing():
     frame = DataFrame(
         {"a": [1, 2, 3], "b": [4, 5, 6], "c": [7.0, 8.0, 9.0]},
@@ -88,7 +104,8 @@ def test_numba_unsupported_dtypes(apply_axis):
     df["c"] = df["c"].astype("double[pyarrow]")
 
     with pytest.raises(
-        ValueError, match="Column b must have a numeric dtype. Found 'object' instead"
+        ValueError,
+        match="Column b must have a numeric dtype. Found 'object|string' instead",
     ):
         df.apply(f, engine="numba", axis=apply_axis)