REF: use should_extension_dispatch for comparison method

pandas/core/ops/__init__.py

@@ -6,6 +6,7 @@
 import datetime
 import operator
 from typing import Any, Callable, Tuple
+import warnings
 
 import numpy as np
 
@@ -23,7 +24,6 @@
     is_bool_dtype,
     is_categorical_dtype,
     is_datetime64_dtype,
-    is_datetime64tz_dtype,
     is_datetimelike_v_numeric,
     is_extension_array_dtype,
     is_integer_dtype,
@@ -32,7 +32,6 @@
     is_period_dtype,
     is_scalar,
     is_timedelta64_dtype,
-    needs_i8_conversion,
 )
 from pandas.core.dtypes.generic import (
     ABCDataFrame,
@@ -605,14 +604,20 @@ def dispatch_to_extension_op(op, left, right):
     new_right = extract_array(right, extract_numpy=True)
 
     try:
-        res_values = op(new_left, new_right)
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            res_values = op(new_left, new_right)
     except NullFrequencyError:
         # DatetimeIndex and TimedeltaIndex with freq == None raise ValueError
         # on add/sub of integers (or int-like).  We re-raise as a TypeError.
         raise TypeError(
             "incompatible type for a datetime/timedelta "
             "operation [{name}]".format(name=op.__name__)
         )
+    if isinstance(res_values, bool):
+        # numpy returned False instead of operating pointwise
+        assert op.__name__ in ["eq", "ne", "ge", "gt", "le", "lt"], op
+        return invalid_comparison(new_left, new_right, op)
     return res_values
 
 
@@ -758,7 +763,6 @@ def _comp_method_SERIES(cls, op, special):
     code duplication.
     """
     op_name = _get_op_name(op, special)
-    masker = _gen_eval_kwargs(op_name).get("masker", False)
 
     def na_op(x, y):
         # TODO:
@@ -777,31 +781,11 @@ def na_op(x, y):
 
         else:
 
-            # we want to compare like types
-            # we only want to convert to integer like if
-            # we are not NotImplemented, otherwise
-            # we would allow datetime64 (but viewed as i8) against
-            # integer comparisons
-
-            # we have a datetime/timedelta and may need to convert
-            assert not needs_i8_conversion(x)
-            mask = None
-            if not is_scalar(y) and needs_i8_conversion(y):
-                mask = isna(x) | isna(y)
-                y = y.view("i8")
-                x = x.view("i8")
-
             method = getattr(x, op_name, None)
-            if method is not None:
-                with np.errstate(all="ignore"):
-                    result = method(y)
-                if result is NotImplemented:
-                    return invalid_comparison(x, y, op)
-            else:
-                result = op(x, y)
-
-            if mask is not None and mask.any():
-                result[mask] = masker
+            with np.errstate(all="ignore"):
+                result = method(y)
+            if result is NotImplemented:
+                return invalid_comparison(x, y, op)
 
         return result
 
@@ -831,12 +815,6 @@ def wrapper(self, other, axis=None):
 
         if isinstance(other, ABCSeries) and not self._indexed_same(other):
             raise ValueError("Can only compare identically-labeled Series objects")
-        elif (
-            is_list_like(other)
-            and len(other) != len(self)
-            and not isinstance(other, (set, frozenset))
-        ):
-            raise ValueError("Lengths must match")
 
         elif isinstance(other, (np.ndarray, ABCIndexClass, ABCSeries)):
             # TODO: make this treatment consistent across ops and classes.
@@ -845,28 +823,7 @@ def wrapper(self, other, axis=None):
             if len(self) != len(other):
                 raise ValueError("Lengths must match to compare")
 
-        if is_categorical_dtype(self):
-            # Dispatch to Categorical implementation; CategoricalIndex
-            # behavior is non-canonical GH#19513
-            res_values = dispatch_to_extension_op(op, self, other)
-
-        elif is_datetime64_dtype(self) or is_datetime64tz_dtype(self):
-            # Dispatch to DatetimeIndex to ensure identical
-            # Series/Index behavior
-            from pandas.core.arrays import DatetimeArray
-
-            res_values = dispatch_to_extension_op(op, DatetimeArray(self), other)
-
-        elif is_timedelta64_dtype(self):
-            from pandas.core.arrays import TimedeltaArray
-
-            res_values = dispatch_to_extension_op(op, TimedeltaArray(self), other)
-
-        elif is_extension_array_dtype(self) or (
-            is_extension_array_dtype(other) and not is_scalar(other)
-        ):
-            # Note: the `not is_scalar(other)` condition rules out
-            #  e.g. other == "category"
+        if should_extension_dispatch(self, other):
             res_values = dispatch_to_extension_op(op, self, other)
 
         elif is_scalar(other) and isna(other):