BUG: DatetimeArray._from_sequence accepting bool dtype

doc/source/whatsnew/v1.1.0.rst

@@ -216,6 +216,7 @@ Datetimelike
 - Bug in :class:`Timestamp` where constructing :class:`Timestamp` with dateutil timezone less than 128 nanoseconds before daylight saving time switch from winter to summer would result in nonexistent time (:issue:`31043`)
 - Bug in :meth:`Period.to_timestamp`, :meth:`Period.start_time` with microsecond frequency returning a timestamp one nanosecond earlier than the correct time (:issue:`31475`)
 - :class:`Timestamp` raising confusing error message when year, month or day is missing (:issue:`31200`)
+- Bug in :class:`DatetimeIndex` constructor incorrectly accepting ``bool``-dtyped inputs (:issue:`32668`)
 
 Timedelta
 ^^^^^^^^^

pandas/core/arrays/datetimes.py

@@ -23,6 +23,7 @@
 from pandas.core.dtypes.common import (
     _INT64_DTYPE,
     _NS_DTYPE,
+    is_bool_dtype,
     is_categorical_dtype,
     is_datetime64_any_dtype,
     is_datetime64_dtype,
@@ -1903,32 +1904,36 @@ def maybe_convert_dtype(data, copy):
     ------
     TypeError : PeriodDType data is passed
     """
-    if is_float_dtype(data):
+    if not hasattr(data, "dtype"):
+        # e.g. collections.deque
+        return data, copy
+
+    if is_float_dtype(data.dtype):
         # Note: we must cast to datetime64[ns] here in order to treat these
         #  as wall-times instead of UTC timestamps.
         data = data.astype(_NS_DTYPE)
         copy = False
         # TODO: deprecate this behavior to instead treat symmetrically
         #  with integer dtypes.  See discussion in GH#23675
 
-    elif is_timedelta64_dtype(data):
+    elif is_timedelta64_dtype(data.dtype) or is_bool_dtype(data.dtype):
         # GH#29794 enforcing deprecation introduced in GH#23539
         raise TypeError(f"dtype {data.dtype} cannot be converted to datetime64[ns]")
-    elif is_period_dtype(data):
+    elif is_period_dtype(data.dtype):
         # Note: without explicitly raising here, PeriodIndex
         #  test_setops.test_join_does_not_recur fails
         raise TypeError(
             "Passing PeriodDtype data is invalid. Use `data.to_timestamp()` instead"
         )
 
-    elif is_categorical_dtype(data):
+    elif is_categorical_dtype(data.dtype):
         # GH#18664 preserve tz in going DTI->Categorical->DTI
         # TODO: cases where we need to do another pass through this func,
         #  e.g. the categories are timedelta64s
         data = data.categories.take(data.codes, fill_value=NaT)._values
         copy = False
 
-    elif is_extension_array_dtype(data) and not is_datetime64tz_dtype(data):
+    elif is_extension_array_dtype(data.dtype) and not is_datetime64tz_dtype(data.dtype):
         # Includes categorical
         # TODO: We have no tests for these
         data = np.array(data, dtype=np.object_)

pandas/core/series.py

@@ -2666,9 +2666,9 @@ def combine(self, other, func, fill_value=None) -> "Series":
                 new_values = [func(lv, other) for lv in self._values]
             new_name = self.name
 
-        if is_categorical_dtype(self.values):
+        if is_categorical_dtype(self.dtype):
             pass
-        elif is_extension_array_dtype(self.values):
+        elif is_extension_array_dtype(self.dtype):
             # The function can return something of any type, so check
             # if the type is compatible with the calling EA.
             new_values = try_cast_to_ea(self._values, new_values)

pandas/core/tools/datetimes.py

@@ -359,7 +359,18 @@ def _convert_listlike_datetimes(
     # warn if passing timedelta64, raise for PeriodDtype
     # NB: this must come after unit transformation
     orig_arg = arg
-    arg, _ = maybe_convert_dtype(arg, copy=False)
+    try:
+        arg, _ = maybe_convert_dtype(arg, copy=False)
+    except TypeError:
+        if errors == "coerce":
+            result = np.array(["NaT"], dtype="datetime64[ns]").repeat(len(arg))
+            return DatetimeIndex(result, name=name)
+        elif errors == "ignore":
+            from pandas import Index
+
+            result = Index(arg, name=name)
+            return result
+        raise
 
     arg = ensure_object(arg)
     require_iso8601 = False

pandas/tests/arrays/test_datetimes.py

@@ -89,11 +89,26 @@ def test_non_array_raises(self):
         with pytest.raises(ValueError, match="list"):
             DatetimeArray([1, 2, 3])
 
-    def test_other_type_raises(self):
+    def test_bool_dtype_raises(self):
+        arr = np.array([1, 2, 3], dtype="bool")
+
         with pytest.raises(
             ValueError, match="The dtype of 'values' is incorrect.*bool"
         ):
-            DatetimeArray(np.array([1, 2, 3], dtype="bool"))
+            DatetimeArray(arr)
+
+        msg = r"dtype bool cannot be converted to datetime64\[ns\]"
+        with pytest.raises(TypeError, match=msg):
+            DatetimeArray._from_sequence(arr)
+
+        with pytest.raises(TypeError, match=msg):
+            sequence_to_dt64ns(arr)
+
+        with pytest.raises(TypeError, match=msg):
+            pd.DatetimeIndex(arr)
+
+        with pytest.raises(TypeError, match=msg):
+            pd.to_datetime(arr)
 
     def test_incorrect_dtype_raises(self):
         with pytest.raises(ValueError, match="Unexpected value for 'dtype'."):