API (string dtype): implement hierarchy (NA > NaN, pyarrow > python) for consistent comparisons between different string dtypes

doc/source/whatsnew/v2.3.0.rst

@@ -50,10 +50,20 @@ Notable bug fixes
 
 These are bug fixes that might have notable behavior changes.
 
-.. _whatsnew_230.notable_bug_fixes.notable_bug_fix1:
+.. _whatsnew_230.notable_bug_fixes.string_comparisons:
 
-notable_bug_fix1
-^^^^^^^^^^^^^^^^
+Comparisons between different string dtypes
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+In previous versions, comparing Series of different string dtypes (e.g. ``pd.StringDtype("pyarrow", na_value=pd.NA)`` against ``pd.StringDtype("python", na_value=np.nan)``) would result in inconsistent resulting dtype or incorrectly raise. pandas will now use the hierarchy
+
+    object < (python, NaN) < (pyarrow, NaN) < (python, NA) < (pyarrow, NA)
+
+in determining the result dtype when there are different string dtypes compared. Some examples:
+
+- When ``pd.StringDtype("pyarrow", na_value=pd.NA)`` is compared against any other string dtype, the result will always be ``boolean[pyarrow]``.
+- When ``pd.StringDtype("python", na_value=pd.NA)`` is compared against ``pd.StringDtype("pyarrow", na_value=np.nan)``, the result will be ``boolean``, the NumPy-backed nullable extension array.
+- When ``pd.StringDtype("python", na_value=pd.NA)`` is compared against ``pd.StringDtype("python", na_value=np.nan)``, the result will be ``boolean``, the NumPy-backed nullable extension array.
 
 .. _whatsnew_230.api_changes:
 

pandas/core/arrays/arrow/array.py

@@ -33,7 +33,6 @@
     infer_dtype_from_scalar,
 )
 from pandas.core.dtypes.common import (
-    CategoricalDtype,
     is_array_like,
     is_bool_dtype,
     is_float_dtype,
@@ -730,9 +729,7 @@ def __setstate__(self, state) -> None:
 
     def _cmp_method(self, other, op) -> ArrowExtensionArray:
         pc_func = ARROW_CMP_FUNCS[op.__name__]
-        if isinstance(
-            other, (ArrowExtensionArray, np.ndarray, list, BaseMaskedArray)
-        ) or isinstance(getattr(other, "dtype", None), CategoricalDtype):
+        if isinstance(other, (ExtensionArray, np.ndarray, list)):
             try:
                 result = pc_func(self._pa_array, self._box_pa(other))
             except pa.ArrowNotImplementedError:

pandas/core/arrays/string_.py

@@ -1015,7 +1015,30 @@ def searchsorted(
         return super().searchsorted(value=value, side=side, sorter=sorter)
 
     def _cmp_method(self, other, op):
-        from pandas.arrays import BooleanArray
+        from pandas.arrays import (
+            ArrowExtensionArray,
+            BooleanArray,
+        )
+
+        if (
+            isinstance(other, BaseStringArray)
+            and self.dtype.na_value is not libmissing.NA
+            and other.dtype.na_value is libmissing.NA
+        ):
+            # NA has priority of NaN semantics
+            return NotImplemented
+
+        if isinstance(other, ArrowExtensionArray):
+            if isinstance(other, BaseStringArray):
+                # pyarrow storage has priority over python storage
+                # (except if we have NA semantics and other not)
+                if not (
+                    self.dtype.na_value is libmissing.NA
+                    and other.dtype.na_value is not libmissing.NA
+                ):
+                    return NotImplemented
+            else:
+                return NotImplemented
 
         if isinstance(other, StringArray):
             other = other._ndarray

pandas/core/arrays/string_arrow.py

@@ -473,6 +473,14 @@ def value_counts(self, dropna: bool = True) -> Series:
         return result
 
     def _cmp_method(self, other, op):
+        if (
+            isinstance(other, (BaseStringArray, ArrowExtensionArray))
+            and self.dtype.na_value is not libmissing.NA
+            and other.dtype.na_value is libmissing.NA
+        ):
+            # NA has priority of NaN semantics
+            return NotImplemented
+
         result = super()._cmp_method(other, op)
         if self.dtype.na_value is np.nan:
             if op == operator.ne:

pandas/core/ops/invalid.py

@@ -25,7 +25,7 @@
 
 def invalid_comparison(
     left: ArrayLike,
-    right: ArrayLike | Scalar,
+    right: ArrayLike | list | Scalar,
     op: Callable[[Any, Any], bool],
 ) -> npt.NDArray[np.bool_]:
     """

pandas/tests/arrays/string_/test_string.py

@@ -10,10 +10,12 @@
 
 from pandas._config import using_string_dtype
 
+from pandas.compat import HAS_PYARROW
 from pandas.compat.pyarrow import (
     pa_version_under12p0,
     pa_version_under19p0,
 )
+import pandas.util._test_decorators as td
 
 from pandas.core.dtypes.common import is_dtype_equal
 
@@ -45,6 +47,25 @@ def cls(dtype):
     return dtype.construct_array_type()
 
 
+def string_dtype_highest_priority(dtype1, dtype2):
+    if HAS_PYARROW:
+        DTYPE_HIERARCHY = [
+            pd.StringDtype("python", na_value=np.nan),
+            pd.StringDtype("pyarrow", na_value=np.nan),
+            pd.StringDtype("python", na_value=pd.NA),
+            pd.StringDtype("pyarrow", na_value=pd.NA),
+        ]
+    else:
+        DTYPE_HIERARCHY = [
+            pd.StringDtype("python", na_value=np.nan),
+            pd.StringDtype("python", na_value=pd.NA),
+        ]
+
+    h1 = DTYPE_HIERARCHY.index(dtype1)
+    h2 = DTYPE_HIERARCHY.index(dtype2)
+    return DTYPE_HIERARCHY[max(h1, h2)]
+
+
 def test_dtype_constructor():
     pytest.importorskip("pyarrow")
 
@@ -331,25 +352,75 @@ def test_comparison_methods_scalar_not_string(comparison_op, dtype):
         tm.assert_extension_array_equal(result, expected)
 
 
-def test_comparison_methods_array(comparison_op, dtype):
+def test_comparison_methods_array(comparison_op, dtype, dtype2):
     op_name = f"__{comparison_op.__name__}__"
 
     a = pd.array(["a", None, "c"], dtype=dtype)
-    other = [None, None, "c"]
-    result = getattr(a, op_name)(other)
-    if dtype.na_value is np.nan:
+    other = pd.array([None, None, "c"], dtype=dtype2)
+    result = comparison_op(a, other)
+
+    # ensure operation is commutative
+    result2 = comparison_op(other, a)
+    tm.assert_equal(result, result2)
+
+    if dtype.na_value is np.nan and dtype2.na_value is np.nan:
         if operator.ne == comparison_op:
             expected = np.array([True, True, False])
         else:
             expected = np.array([False, False, False])
             expected[-1] = getattr(other[-1], op_name)(a[-1])
         tm.assert_numpy_array_equal(result, expected)
 
-        result = getattr(a, op_name)(pd.NA)
+    else:
+        max_dtype = string_dtype_highest_priority(dtype, dtype2)
+        if max_dtype.storage == "python":
+            expected_dtype = "boolean"
+        else:
+            expected_dtype = "bool[pyarrow]"
+
+        expected = np.full(len(a), fill_value=None, dtype="object")
+        expected[-1] = getattr(other[-1], op_name)(a[-1])
+        expected = pd.array(expected, dtype=expected_dtype)
+        tm.assert_extension_array_equal(result, expected)
+
+
+@td.skip_if_no("pyarrow")
+def test_comparison_methods_array_arrow_extension(comparison_op, dtype2):
+    # Test pd.ArrowDtype(pa.string()) against other string arrays
+    import pyarrow as pa
+
+    op_name = f"__{comparison_op.__name__}__"
+    dtype = pd.ArrowDtype(pa.string())
+    a = pd.array(["a", None, "c"], dtype=dtype)
+    other = pd.array([None, None, "c"], dtype=dtype2)
+    result = comparison_op(a, other)
+
+    # ensure operation is commutative
+    result2 = comparison_op(other, a)
+    tm.assert_equal(result, result2)
+
+    expected = pd.array([None, None, True], dtype="bool[pyarrow]")
+    expected[-1] = getattr(other[-1], op_name)(a[-1])
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_comparison_methods_list(comparison_op, dtype):
+    op_name = f"__{comparison_op.__name__}__"
+
+    a = pd.array(["a", None, "c"], dtype=dtype)
+    other = [None, None, "c"]
+    result = comparison_op(a, other)
+
+    # ensure operation is commutative
+    result2 = comparison_op(other, a)
+    tm.assert_equal(result, result2)
+
+    if dtype.na_value is np.nan:
         if operator.ne == comparison_op:
-            expected = np.array([True, True, True])
+            expected = np.array([True, True, False])
         else:
             expected = np.array([False, False, False])
+            expected[-1] = getattr(other[-1], op_name)(a[-1])
         tm.assert_numpy_array_equal(result, expected)
 
     else:
@@ -359,10 +430,6 @@ def test_comparison_methods_array(comparison_op, dtype):
         expected = pd.array(expected, dtype=expected_dtype)
         tm.assert_extension_array_equal(result, expected)
 
-        result = getattr(a, op_name)(pd.NA)
-        expected = pd.array([None, None, None], dtype=expected_dtype)
-        tm.assert_extension_array_equal(result, expected)
-
 
 def test_constructor_raises(cls):
     if cls is pd.arrays.StringArray:

pandas/tests/extension/test_string.py

@@ -31,6 +31,7 @@
 from pandas.api.types import is_string_dtype
 from pandas.core.arrays import ArrowStringArray
 from pandas.core.arrays.string_ import StringDtype
+from pandas.tests.arrays.string_.test_string import string_dtype_highest_priority
 from pandas.tests.extension import base
 
 
@@ -202,10 +203,13 @@ def _cast_pointwise_result(self, op_name: str, obj, other, pointwise_result):
         dtype = cast(StringDtype, tm.get_dtype(obj))
         if op_name in ["__add__", "__radd__"]:
             cast_to = dtype
+            dtype_other = tm.get_dtype(other) if not isinstance(other, str) else None
+            if isinstance(dtype_other, StringDtype):
+                cast_to = string_dtype_highest_priority(dtype, dtype_other)
         elif dtype.na_value is np.nan:
             cast_to = np.bool_  # type: ignore[assignment]
         elif dtype.storage == "pyarrow":
-            cast_to = "boolean[pyarrow]"  # type: ignore[assignment]
+            cast_to = "bool[pyarrow]"  # type: ignore[assignment]
         else:
             cast_to = "boolean"  # type: ignore[assignment]
         return pointwise_result.astype(cast_to)
@@ -236,10 +240,10 @@ def test_arith_series_with_array(
         if (
             using_infer_string
             and all_arithmetic_operators == "__radd__"
-            and (
-                (dtype.na_value is pd.NA) or (dtype.storage == "python" and HAS_PYARROW)
-            )
+            and dtype.na_value is pd.NA
+            and (HAS_PYARROW or dtype.storage == "pyarrow")
         ):
+            # TODO(infer_string)
             mark = pytest.mark.xfail(
                 reason="The pointwise operation result will be inferred to "
                 "string[nan, pyarrow], which does not match the input dtype"