BUG/PERF: Avoid listifying in dispatch_to_extension_op

doc/source/whatsnew/v0.24.0.txt

@@ -845,6 +845,7 @@ Numeric
 - Bug in :meth:`DataFrame.apply` where, when supplied with a string argument and additional positional or keyword arguments (e.g. ``df.apply('sum', min_count=1)``), a ``TypeError`` was wrongly raised (:issue:`22376`)
 - Bug in :meth:`DataFrame.astype` to extension dtype may raise ``AttributeError`` (:issue:`22578`)
 - Bug in :class:`DataFrame` with ``timedelta64[ns]`` dtype arithmetic operations with ``ndarray`` with integer dtype incorrectly treating the narray as ``timedelta64[ns]`` dtype (:issue:`23114`)
+- Bug in :meth:`Series.rpow` with object dtype ``NaN`` for ``1 ** NA`` instead of ``1`` (:issue:`22922`).
 
 Strings
 ^^^^^^^

pandas/core/arrays/integer.py

@@ -280,6 +280,8 @@ def _coerce_to_ndarray(self):
         data[self._mask] = self._na_value
         return data
 
+    __array_priority__ = 1  # higher than ndarray so ops dispatch to us
+
     def __array__(self, dtype=None):
         """
         the array interface, return my values
@@ -288,12 +290,6 @@ def __array__(self, dtype=None):
         return self._coerce_to_ndarray()
 
     def __iter__(self):
-        """Iterate over elements of the array.
-
-        """
-        # This needs to be implemented so that pandas recognizes extension
-        # arrays as list-like. The default implementation makes successive
-        # calls to ``__getitem__``, which may be slower than necessary.
         for i in range(len(self)):
             if self._mask[i]:
                 yield self.dtype.na_value
@@ -504,8 +500,13 @@ def cmp_method(self, other):
 
             op_name = op.__name__
             mask = None
+
             if isinstance(other, IntegerArray):
                 other, mask = other._data, other._mask
+
+            elif getattr(other, 'ndim', None) == 0:
+                other = other.item()
+
             elif is_list_like(other):
                 other = np.asarray(other)
                 if other.ndim > 0 and len(self) != len(other):
@@ -586,14 +587,20 @@ def integer_arithmetic_method(self, other):
 
             op_name = op.__name__
             mask = None
+
+            if getattr(other, 'ndim', 0) > 1:
+                raise NotImplementedError(
+                    "can only perform ops with 1-d structures")
+
             if isinstance(other, (ABCSeries, ABCIndexClass)):
                 other = getattr(other, 'values', other)
 
             if isinstance(other, IntegerArray):
                 other, mask = other._data, other._mask
-            elif getattr(other, 'ndim', 0) > 1:
-                raise NotImplementedError(
-                    "can only perform ops with 1-d structures")
+
+            elif getattr(other, 'ndim', None) == 0:
+                other = other.item()
+
             elif is_list_like(other):
                 other = np.asarray(other)
                 if not other.ndim:
@@ -612,6 +619,10 @@ def integer_arithmetic_method(self, other):
             else:
                 mask = self._mask | mask
 
+            if op_name == 'rpow':
+                # 1 ** np.nan is 1. So we have to unmask those.
+                mask = np.where(other == 1, False, mask)
+
             with np.errstate(all='ignore'):
                 result = op(self._data, other)
 

pandas/core/ops.py

@@ -862,10 +862,16 @@ def masked_arith_op(x, y, op):
         # mask is only meaningful for x
         result = np.empty(x.size, dtype=x.dtype)
         mask = notna(xrav)
+
         if mask.any():
             with np.errstate(all='ignore'):
                 result[mask] = op(xrav[mask], y)
 
+        if op == pow:
+            result = np.where(~mask, x, result)
+        elif op == rpow:
+            result = np.where(~mask, y, result)
+
     result, changed = maybe_upcast_putmask(result, ~mask, np.nan)
     result = result.reshape(x.shape)  # 2D compat
     return result
@@ -1202,29 +1208,16 @@ def dispatch_to_extension_op(op, left, right):
 
     # The op calls will raise TypeError if the op is not defined
     # on the ExtensionArray
-    # TODO(jreback)
-    # we need to listify to avoid ndarray, or non-same-type extension array
-    # dispatching
-
-    if is_extension_array_dtype(left):
-
-        new_left = left.values
-        if isinstance(right, np.ndarray):
-
-            # handle numpy scalars, this is a PITA
-            # TODO(jreback)
-            new_right = lib.item_from_zerodim(right)
-            if is_scalar(new_right):
-                new_right = [new_right]
-            new_right = list(new_right)
-        elif is_extension_array_dtype(right) and type(left) != type(right):
-            new_right = list(right)
-        else:
-            new_right = right
 
+    # unbox Series and Index to arrays
+    if isinstance(left, (ABCSeries, ABCIndexClass)):
+        new_left = left._values
     else:
+        new_left = left
 
-        new_left = list(left.values)
+    if isinstance(right, (ABCSeries, ABCIndexClass)):
+        new_right = right._values
+    else:
         new_right = right
 
     res_values = op(new_left, new_right)

pandas/tests/arrays/test_integer.py

@@ -218,25 +218,68 @@ def test_arith_integer_array(self, data, all_arithmetic_operators):
     def test_arith_series_with_scalar(self, data, all_arithmetic_operators):
         # scalar
         op = all_arithmetic_operators
+        if op == '__rpow__':
+            raise pytest.skip("__rpow__ tested separately.")
 
         s = pd.Series(data)
         self._check_op(s, op, 1, exc=TypeError)
 
+    def test_arith_series_with_scalar_rpow(self, data):
+        s = pd.Series(data)
+        # 1^x is 1.0 for all x, so test separately
+        result = 1 ** s
+        expected = pd.Series(1, index=s.index, dtype=data.dtype.numpy_dtype)
+        assert result.dtype == data.dtype
+        result = result.astype(data.dtype.numpy_dtype)
+        self.assert_series_equal(result, expected)
+
+        # test other bases regularly
+        self._check_op(s, '__rpow__', 2, exc=None)
+
     def test_arith_frame_with_scalar(self, data, all_arithmetic_operators):
         # frame & scalar
         op = all_arithmetic_operators
 
+        if op == '__rpow__':
+            raise pytest.skip("__rpow__ tested separately.")
+
         df = pd.DataFrame({'A': data})
         self._check_op(df, op, 1, exc=TypeError)
 
+    def test_arith_frame_with_scalar_rpow(self, data):
+        df = pd.DataFrame({"A": data})
+        result = 1.0 ** df
+        expected = pd.DataFrame(1.0, index=df.index, columns=df.columns)
+        self.assert_frame_equal(result, expected)
+
+        # test other bases regularly
+        self._check_op(df, '__rpow__', 2, exc=TypeError)
+
     def test_arith_series_with_array(self, data, all_arithmetic_operators):
         # ndarray & other series
         op = all_arithmetic_operators
 
+        if op == '__rpow__':
+            raise pytest.skip("__rpow__ tested separately.")
+
         s = pd.Series(data)
         other = np.ones(len(s), dtype=s.dtype.type)
         self._check_op(s, op, other, exc=TypeError)
 
+    def test_arith_series_with_array_rpow(self, data):
+        s = pd.Series(data)
+        other = np.ones(len(s), dtype=data.dtype.numpy_dtype)
+        expected = pd.Series(1, index=s.index, dtype=data.dtype.numpy_dtype)
+        result = other ** s
+
+        assert result.dtype == data.dtype
+        result = result.astype(data.dtype.numpy_dtype)
+
+        self.assert_series_equal(result, expected)
+
+        other = 2 * np.ones(len(s), dtype=s.dtype.type)
+        self._check_op(s, '__rpow__', other, exc=TypeError)
+
     def test_arith_coerce_scalar(self, data, all_arithmetic_operators):
 
         op = all_arithmetic_operators
@@ -248,13 +291,20 @@ def test_arith_coerce_scalar(self, data, all_arithmetic_operators):
     @pytest.mark.parametrize("other", [1., 1.0, np.array(1.), np.array([1.])])
     def test_arithmetic_conversion(self, all_arithmetic_operators, other):
         # if we have a float operand we should have a float result
-        # if if that is equal to an integer
+        # if that is equal to an integer
         op = self.get_op_from_name(all_arithmetic_operators)
 
         s = pd.Series([1, 2, 3], dtype='Int64')
         result = op(s, other)
         assert result.dtype is np.dtype('float')
 
+    @pytest.mark.parametrize("other", [0, 0.5])
+    def test_arith_zero_dim_ndarray(self, other):
+        arr = integer_array([1, None, 2])
+        result = arr + np.array(other)
+        expected = arr + other
+        tm.assert_equal(result, expected)
+
     def test_error(self, data, all_arithmetic_operators):
         # invalid ops
 
@@ -323,6 +373,14 @@ def test_compare_array(self, data, all_compare_operators):
         other = pd.Series([0] * len(data))
         self._compare_other(s, data, op_name, other)
 
+    def test_rpow_one_to_na(self):
+        # https://github.com/pandas-dev/pandas/issues/22022
+        # NumPy says 1 ** nan is 1.
+        arr = integer_array([np.nan, np.nan])
+        result = np.array([1.0, 2.0]) ** arr
+        expected = np.array([1.0, np.nan])
+        tm.assert_numpy_array_equal(result, expected)
+
 
 class TestCasting(object):
     pass

pandas/tests/extension/test_integer.py

@@ -143,7 +143,6 @@ def test_error(self, data, all_arithmetic_operators):
         # other specific errors tested in the integer array specific tests
         pass
 
-    @pytest.mark.xfail(reason="EA is listified. GH-22922", strict=True)
     def test_add_series_with_extension_array(self, data):
         super(TestArithmeticOps, self).test_add_series_with_extension_array(
             data