Cleanup

pandas/core/arrays/datetimelike.py

@@ -42,21 +42,10 @@
 class AttributesMixin:  # TODO: how much of this do we still need?
     _data = None  # type: np.ndarray
 
-    @property
-    def _attributes(self):
-        # Inheriting subclass should implement _attributes as a list of strings
-        raise AbstractMethodError(self)
-
     @classmethod
     def _simple_new(cls, values, **kwargs):
         raise AbstractMethodError(cls)
 
-    def _get_attributes_dict(self):
-        """
-        return an attributes dict for my class
-        """
-        return {k: getattr(self, k, None) for k in self._attributes}
-
     @property
     def _scalar_type(self) -> Type[DatetimeLikeScalar]:
         """The scalar associated with this datelike

pandas/core/arrays/reshaping.py

@@ -80,8 +80,7 @@ def shift(self, periods: int = 1, fill_value: object = None):
         # FIXME: technically wrong to allow if we dont have ndim == 1
 
         result = self._1dvalues.shift(periods, fill_value=fill_value)
-        shape = self.shape
-        return type(self)(result, shape=shape)
+        return type(self)(result, shape=self.shape)
 
     # --------------------------------------------------
     # Lightly Modified pass-through methods
@@ -127,11 +126,7 @@ def __sub__(self, other):
         return type(self)(result, shape=self.shape)
 
     def __array__(self, dtype=None):
-        if hasattr(self._1dvalues, "__array__"):
-            result = self._1dvalues.__array__(dtype=dtype)
-        else:
-            result = np.array(self._1dvalues, dtype=dtype)
-            # TODO: cant we use this unconditionally?
+        result = np.array(self._1dvalues, dtype=dtype)
         return result.reshape(self.shape)
 
     def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
@@ -472,10 +467,8 @@ def _tuplify_shape(size: int, shape) -> Tuple[int, ...]:
     return shape
 
 
-def unwrap_reshapeable(values, check=True):
+def unwrap_reshapeable(values):
     if isinstance(values, ReshapeableArray):
-        # FIXME: re-enablen check
-        # if check:
-        #    assert values.ndim == 1
+        # TODO: require we are only working with 1D?
         return values._1dvalues
     return values

pandas/core/internals/blocks.py

@@ -1651,7 +1651,7 @@ def _maybe_coerce_values(self, values):
     @property
     def _holder(self):
         # For extension blocks, the holder is values-dependent.
-        return type(unwrap_reshapeable(self.values, check=False))
+        return type(unwrap_reshapeable(self.values))
 
     @property
     def fill_value(self):