BUG: to_numeric fails to convert a Pyarrow Decimal series containing NA values

doc/source/whatsnew/v3.0.0.rst

@@ -1240,6 +1240,7 @@ Other
 - Bug in :func:`eval` where the names of the :class:`Series` were not preserved when using ``engine="numexpr"``. (:issue:`10239`)
 - Bug in :func:`eval` with ``engine="numexpr"`` returning unexpected result for float division. (:issue:`59736`)
 - Bug in :func:`to_numeric` raising ``TypeError`` when ``arg`` is a :class:`Timedelta` or :class:`Timestamp` scalar. (:issue:`59944`)
+- Bug in :func:`to_numeric` with :class:`ArrowDtype` raising ``ValueError`` when the array contained NA values. (:issue:`61641`)
 - Bug in :func:`unique` on :class:`Index` not always returning :class:`Index` (:issue:`57043`)
 - Bug in :meth:`DataFrame.apply` raising ``RecursionError`` when passing ``func=list[int]``. (:issue:`61565`)
 - Bug in :meth:`DataFrame.apply` where passing ``engine="numba"`` ignored ``args`` passed to the applied function (:issue:`58712`)

pandas/core/tools/numeric.py

@@ -222,8 +222,12 @@ def to_numeric(
 
     values_dtype = getattr(values, "dtype", None)
     if isinstance(values_dtype, ArrowDtype):
+        if is_numeric_dtype(values_dtype) and is_series:
+            return arg._constructor(values, index=arg.index, name=arg.name)
+
         mask = values.isna()
         values = values.dropna().to_numpy()
+
     new_mask: np.ndarray | None = None
     if is_numeric_dtype(values_dtype):
         pass

pandas/tests/tools/test_to_numeric.py

@@ -902,3 +902,20 @@ def test_coerce_pyarrow_backend():
     result = to_numeric(ser, errors="coerce", dtype_backend="pyarrow")
     expected = Series([1, 2, None], dtype=ArrowDtype(pa.int64()))
     tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        "ArrowDtype",
+    ],
+)
+def test_to_numeric_arrow_decimal_with_na(dtype):
+    # GH 61641
+    pa = pytest.importorskip("pyarrow")
+    target_class = globals()[dtype]
+    decimal_type = target_class(pa.decimal128(3, scale=2))
+    series = Series([1, None], dtype=decimal_type)
+    result = to_numeric(series, errors="coerce")
+
+    tm.assert_series_equal(result, series)