CLN: unnecessary checks

pandas/core/arrays/categorical.py

@@ -321,7 +321,7 @@ def __init__(
         if is_categorical_dtype(values):
             if dtype.categories is None:
                 dtype = CategoricalDtype(values.categories, dtype.ordered)
-        elif not isinstance(values, (ABCIndex, ABCSeries)):
+        elif not isinstance(values, (ABCIndex, ABCSeries, ExtensionArray)):
             # sanitize_array coerces np.nan to a string under certain versions
             # of numpy
             values = maybe_infer_to_datetimelike(values, convert_dates=True)

pandas/core/dtypes/cast.py

@@ -80,11 +80,8 @@
 )
 from pandas.core.dtypes.generic import (
     ABCDataFrame,
-    ABCDatetimeArray,
-    ABCDatetimeIndex,
     ABCExtensionArray,
-    ABCPeriodArray,
-    ABCPeriodIndex,
+    ABCIndex,
     ABCSeries,
 )
 from pandas.core.dtypes.inference import is_list_like
@@ -1252,11 +1249,9 @@ def maybe_infer_to_datetimelike(
        leave inferred dtype 'date' alone
 
     """
-    # TODO: why not timedelta?
-    if isinstance(
-        value, (ABCDatetimeIndex, ABCPeriodIndex, ABCDatetimeArray, ABCPeriodArray)
-    ):
-        return value
+    if isinstance(value, (ABCIndex, ABCExtensionArray)):
+        if not is_object_dtype(value.dtype):
+            raise ValueError("array-like value must be object-dtype")
 
     v = value
 
@@ -1431,7 +1426,7 @@ def maybe_cast_to_datetime(value, dtype: Optional[DtypeObj]):
                             value = to_timedelta(value, errors="raise")._values
                     except OutOfBoundsDatetime:
                         raise
-                    except (AttributeError, ValueError, TypeError):
+                    except (ValueError, TypeError):
                         pass
 
         # coerce datetimelike to object

pandas/core/internals/construction.py

@@ -212,7 +212,7 @@ def init_ndarray(values, index, columns, dtype: Optional[DtypeObj], copy: bool):
     # if we don't have a dtype specified, then try to convert objects
     # on the entire block; this is to convert if we have datetimelike's
     # embedded in an object type
-    if dtype is None and is_object_dtype(values):
+    if dtype is None and is_object_dtype(values.dtype):
 
         if values.ndim == 2 and values.shape[0] != 1:
             # transpose and separate blocks