diff --git a/pandas/core/arrays/categorical.py b/pandas/core/arrays/categorical.py
index 95d9409b265ce..47779dd6dba25 100644
--- a/pandas/core/arrays/categorical.py
+++ b/pandas/core/arrays/categorical.py
@@ -439,12 +439,6 @@ def __init__(
                         "explicitly specify the categories order "
                         "by passing in a categories argument."
                     ) from err
-            except ValueError as err:
-
-                # TODO(EA2D)
-                raise NotImplementedError(
-                    "> 1 ndim Categorical are not supported at this time"
-                ) from err
 
             # we're inferring from values
             dtype = CategoricalDtype(categories, dtype.ordered)
diff --git a/pandas/core/common.py b/pandas/core/common.py
index 04ff2d2c4618f..c0e44a437f59e 100644
--- a/pandas/core/common.py
+++ b/pandas/core/common.py
@@ -142,11 +142,8 @@ def is_bool_indexer(key: Any) -> bool:
         elif is_bool_dtype(key.dtype):
             return True
     elif isinstance(key, list):
-        try:
-            arr = np.asarray(key)
-            return arr.dtype == np.bool_ and len(arr) == len(key)
-        except TypeError:  # pragma: no cover
-            return False
+        arr = np.asarray(key)
+        return arr.dtype == np.bool_ and len(arr) == len(key)
 
     return False
 
diff --git a/pandas/core/construction.py b/pandas/core/construction.py
index 0267116cdfb99..62b75dd90c79b 100644
--- a/pandas/core/construction.py
+++ b/pandas/core/construction.py
@@ -17,7 +17,6 @@
 import numpy.ma as ma
 
 from pandas._libs import lib
-from pandas._libs.tslibs import IncompatibleFrequency
 from pandas._typing import (
     AnyArrayLike,
     ArrayLike,
@@ -289,9 +288,9 @@ def array(
         IntegerArray,
         IntervalArray,
         PandasArray,
+        PeriodArray,
         StringArray,
         TimedeltaArray,
-        period_array,
     )
 
     if lib.is_scalar(data):
@@ -315,12 +314,8 @@ def array(
     if dtype is None:
         inferred_dtype = lib.infer_dtype(data, skipna=True)
         if inferred_dtype == "period":
-            try:
-                return period_array(data, copy=copy)
-            except IncompatibleFrequency:
-                # We may have a mixture of frequencies.
-                # We choose to return an ndarray, rather than raising.
-                pass
+            return PeriodArray._from_sequence(data, copy=copy)
+
         elif inferred_dtype == "interval":
             try:
                 return IntervalArray(data, copy=copy)
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 2a50ebd959ace..11f4da02c0d2f 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -6468,11 +6468,8 @@ def _maybe_cast_data_without_dtype(subarr: np.ndarray) -> ArrayLike:
             tda = TimedeltaArray._from_sequence(subarr, copy=False)
             return tda
         elif inferred == "period":
-            try:
-                parr = PeriodArray._from_sequence(subarr)
-                return parr
-            except IncompatibleFrequency:
-                pass
+            parr = PeriodArray._from_sequence(subarr)
+            return parr
 
     return subarr