REGR: Assigning label with registered EA dtype raises

doc/source/whatsnew/v1.2.0.rst

@@ -856,7 +856,7 @@ Other
 - Bug in :meth:`Index.drop` raising ``InvalidIndexError`` when index has duplicates (:issue:`38051`)
 - Bug in :meth:`RangeIndex.difference` returning :class:`Int64Index` in some cases where it should return :class:`RangeIndex` (:issue:`38028`)
 - Fixed bug in :func:`assert_series_equal` when comparing a datetime-like array with an equivalent non extension dtype array (:issue:`37609`)
-
+- Bug in :func:`.is_bool_dtype` would raise when passed a valid string such as ``"boolean"`` (:issue:`38386`).
 
 
 .. ---------------------------------------------------------------------------

pandas/core/dtypes/common.py

@@ -1397,7 +1397,7 @@ def is_bool_dtype(arr_or_dtype) -> bool:
         # guess this
         return arr_or_dtype.is_object and arr_or_dtype.inferred_type == "boolean"
     elif is_extension_array_dtype(arr_or_dtype):
-        return getattr(arr_or_dtype, "dtype", arr_or_dtype)._is_boolean
+        return getattr(dtype, "_is_boolean", False)
 
     return issubclass(dtype.type, np.bool_)
 

pandas/io/parsers.py

@@ -1689,9 +1689,8 @@ def _convert_to_ndarrays(
                     values, set(col_na_values) | col_na_fvalues, try_num_bool=False
                 )
             else:
-                is_str_or_ea_dtype = is_string_dtype(
-                    cast_type
-                ) or is_extension_array_dtype(cast_type)
+                is_ea = is_extension_array_dtype(cast_type)
+                is_str_or_ea_dtype = is_string_dtype(cast_type) or is_ea
                 # skip inference if specified dtype is object
                 # or casting to an EA
                 try_num_bool = not (cast_type and is_str_or_ea_dtype)
@@ -1707,11 +1706,7 @@ def _convert_to_ndarrays(
                     or is_extension_array_dtype(cast_type)
                 ):
                     try:
-                        if (
-                            is_bool_dtype(cast_type)
-                            and not is_categorical_dtype(cast_type)
-                            and na_count > 0
-                        ):
+                        if not is_ea and na_count > 0 and is_bool_dtype(cast_type):
                             raise ValueError(f"Bool column has NA values in column {c}")
                     except (AttributeError, TypeError):
                         # invalid input to is_bool_dtype

pandas/tests/dtypes/test_common.py

@@ -545,6 +545,7 @@ def test_is_bool_dtype():
     assert not com.is_bool_dtype(pd.Series([1, 2]))
     assert not com.is_bool_dtype(np.array(["a", "b"]))
     assert not com.is_bool_dtype(pd.Index(["a", "b"]))
+    assert not com.is_bool_dtype("Int64")
 
     assert com.is_bool_dtype(bool)
     assert com.is_bool_dtype(np.bool_)
@@ -553,6 +554,7 @@ def test_is_bool_dtype():
 
     assert com.is_bool_dtype(pd.BooleanDtype())
     assert com.is_bool_dtype(pd.array([True, False, None], dtype="boolean"))
+    assert com.is_bool_dtype("boolean")
 
 
 @pytest.mark.filterwarnings("ignore:'is_extension_type' is deprecated:FutureWarning")

pandas/tests/frame/indexing/test_setitem.py

@@ -1,6 +1,7 @@
 import numpy as np
 import pytest
 
+from pandas.core.dtypes.base import registry as ea_registry
 from pandas.core.dtypes.dtypes import DatetimeTZDtype, IntervalDtype, PeriodDtype
 
 from pandas import (
@@ -197,6 +198,22 @@ def test_setitem_extension_types(self, obj, dtype):
 
         tm.assert_frame_equal(df, expected)
 
+    @pytest.mark.parametrize(
+        "ea_name",
+        # property would require instantiation
+        [
+            dtype.name
+            for dtype in ea_registry.dtypes
+            if not isinstance(dtype.name, property)
+        ],
+    )
+    def test_setitem_with_ea_name(self, ea_name):
+        # GH 38386
+        result = DataFrame([0])
+        result[ea_name] = [1]
+        expected = DataFrame({0: [0], ea_name: [1]})
+        tm.assert_frame_equal(result, expected)
+
     def test_setitem_dt64_ndarray_with_NaT_and_diff_time_units(self):
         # GH#7492
         data_ns = np.array([1, "nat"], dtype="datetime64[ns]")