use numexpr for Series comparisons

pandas/core/ops/__init__.py

@@ -374,7 +374,6 @@ def dispatch_to_series(left, right, func, str_rep=None, axis=None):
     """
     # Note: we use iloc to access columns for compat with cases
     #       with non-unique columns.
-    import pandas.core.computation.expressions as expressions
 
     right = lib.item_from_zerodim(right)
     if lib.is_scalar(right) or np.ndim(right) == 0:
@@ -419,8 +418,7 @@ def column_op(a, b):
         # Remaining cases have less-obvious dispatch rules
         raise NotImplementedError(right)
 
-    new_data = expressions.evaluate(column_op, str_rep, left, right)
-    return new_data
+    return column_op(left, right)
 
 
 # -----------------------------------------------------------------------------
@@ -515,6 +513,7 @@ def _comp_method_SERIES(cls, op, special):
     Wrapper function for Series arithmetic operations, to avoid
     code duplication.
     """
+    str_rep = _get_opstr(op)
     op_name = _get_op_name(op, special)
 
     @unpack_zerodim_and_defer(op_name)
@@ -528,7 +527,7 @@ def wrapper(self, other):
         lvalues = extract_array(self, extract_numpy=True)
         rvalues = extract_array(other, extract_numpy=True)
 
-        res_values = comparison_op(lvalues, rvalues, op)
+        res_values = comparison_op(lvalues, rvalues, op, str_rep)
 
         return _construct_result(self, res_values, index=self.index, name=res_name)
 

pandas/core/ops/array_ops.py

@@ -125,7 +125,7 @@ def na_op(x, y):
     return na_op
 
 
-def na_arithmetic_op(left, right, op, str_rep: str):
+def na_arithmetic_op(left, right, op, str_rep: Optional[str], is_cmp: bool = False):
     """
     Return the result of evaluating op on the passed in values.
 
@@ -136,6 +136,8 @@ def na_arithmetic_op(left, right, op, str_rep: str):
     left : np.ndarray
     right : np.ndarray or scalar
     str_rep : str or None
+    is_cmp : bool, default False
+        If this a comparison operation.
 
     Returns
     -------
@@ -150,6 +152,8 @@ def na_arithmetic_op(left, right, op, str_rep: str):
     try:
         result = expressions.evaluate(op, str_rep, left, right)
     except TypeError:
+        if is_cmp:
+            raise
         result = masked_arith_op(left, right, op)
 
     return missing.dispatch_fill_zeros(op, left, right, result)
@@ -201,7 +205,10 @@ def arithmetic_op(
 
 
 def comparison_op(
-    left: Union[np.ndarray, ABCExtensionArray], right: Any, op
+    left: Union[np.ndarray, ABCExtensionArray],
+    right: Any,
+    op,
+    str_rep: Optional[str] = None,
 ) -> Union[np.ndarray, ABCExtensionArray]:
     """
     Evaluate a comparison operation `=`, `!=`, `>=`, `>`, `<=`, or `<`.
@@ -250,7 +257,10 @@ def comparison_op(
         op_name = f"__{op.__name__}__"
         method = getattr(lvalues, op_name)
         with np.errstate(all="ignore"):
-            res_values = method(rvalues)
+            res_values = na_arithmetic_op(lvalues, rvalues, op, str_rep, is_cmp=True)
+            if is_scalar(res_values):
+                # numexpr choked
+                res_values = method(rvalues)
 
         if res_values is NotImplemented:
             res_values = invalid_comparison(lvalues, rvalues, op)
@@ -385,7 +395,7 @@ def get_array_op(op, str_rep: Optional[str] = None):
     """
     op_name = op.__name__.strip("_")
     if op_name in {"eq", "ne", "lt", "le", "gt", "ge"}:
-        return partial(comparison_op, op=op)
+        return partial(comparison_op, op=op, str_rep=str_rep)
     elif op_name in {"and", "or", "xor", "rand", "ror", "rxor"}:
         return partial(logical_op, op=op)
     else:

pandas/tests/arithmetic/test_numeric.py

@@ -66,7 +66,12 @@ def test_df_numeric_cmp_dt64_raises(self):
         ts = pd.Timestamp.now()
         df = pd.DataFrame({"x": range(5)})
 
-        msg = "Invalid comparison between dtype=int64 and Timestamp"
+        msg = "|".join(
+            [
+                "Invalid comparison between dtype=int64 and Timestamp",
+                "'[<>]' not supported between instances of 'Timestamp' and 'int'",
+            ]
+        )
 
         with pytest.raises(TypeError, match=msg):
             df > ts