DOC: Update variables a and b to names consistent with comment documentation

pandas/core/computation/eval.py

@@ -371,10 +371,12 @@ def eval(
                 is_extension_array_dtype(parsed_expr.terms.return_type)
                 and not is_string_dtype(parsed_expr.terms.return_type)
             )
-            or getattr(parsed_expr.terms, "operand_types", None) is not None
-            and any(
-                (is_extension_array_dtype(elem) and not is_string_dtype(elem))
-                for elem in parsed_expr.terms.operand_types
+            or (
+                getattr(parsed_expr.terms, "operand_types", None) is not None
+                and any(
+                    (is_extension_array_dtype(elem) and not is_string_dtype(elem))
+                    for elem in parsed_expr.terms.operand_types
+                )
             )
         ):
             warnings.warn(

pandas/core/computation/expr.py

@@ -512,8 +512,7 @@ def _maybe_evaluate_binop(
             )
 
         if self.engine != "pytables" and (
-            res.op in CMP_OPS_SYMS
-            and getattr(lhs, "is_datetime", False)
+            (res.op in CMP_OPS_SYMS and getattr(lhs, "is_datetime", False))
             or getattr(rhs, "is_datetime", False)
         ):
             # all date ops must be done in python bc numexpr doesn't work

pandas/core/computation/expressions.py

@@ -65,23 +65,23 @@ def set_numexpr_threads(n=None) -> None:
         ne.set_num_threads(n)
 
 
-def _evaluate_standard(op, op_str, a, b):
+def _evaluate_standard(op, op_str, left_op, right_op):
     """
     Standard evaluation.
     """
     if _TEST_MODE:
         _store_test_result(False)
-    return op(a, b)
+    return op(left_op, right_op)
 
 
-def _can_use_numexpr(op, op_str, a, b, dtype_check) -> bool:
+def _can_use_numexpr(op, op_str, left_op, right_op, dtype_check) -> bool:
     """return a boolean if we WILL be using numexpr"""
     if op_str is not None:
         # required min elements (otherwise we are adding overhead)
-        if a.size > _MIN_ELEMENTS:
+        if left_op.size > _MIN_ELEMENTS:
             # check for dtype compatibility
             dtypes: set[str] = set()
-            for o in [a, b]:
+            for o in [left_op, right_op]:
                 # ndarray and Series Case
                 if hasattr(o, "dtype"):
                     dtypes |= {o.dtype.name}
@@ -93,43 +93,43 @@ def _can_use_numexpr(op, op_str, a, b, dtype_check) -> bool:
     return False
 
 
-def _evaluate_numexpr(op, op_str, a, b):
+def _evaluate_numexpr(op, op_str, left_op, right_op):
     result = None
 
-    if _can_use_numexpr(op, op_str, a, b, "evaluate"):
+    if _can_use_numexpr(op, op_str, left_op, right_op, "evaluate"):
         is_reversed = op.__name__.strip("_").startswith("r")
         if is_reversed:
             # we were originally called by a reversed op method
-            a, b = b, a
+            left_op, right_op = right_op, left_op
 
-        a_value = a
-        b_value = b
+        left_value = left_op
+        right_value = right_op
 
         try:
             result = ne.evaluate(
-                f"a_value {op_str} b_value",
-                local_dict={"a_value": a_value, "b_value": b_value},
+                f"left_value {op_str} right_value",
+                local_dict={"left_value": left_value, "right_value": right_value},
                 casting="safe",
             )
         except TypeError:
             # numexpr raises eg for array ** array with integers
             # (https://github.com/pydata/numexpr/issues/379)
             pass
         except NotImplementedError:
-            if _bool_arith_fallback(op_str, a, b):
+            if _bool_arith_fallback(op_str, left_op, right_op):
                 pass
             else:
                 raise
 
         if is_reversed:
             # reverse order to original for fallback
-            a, b = b, a
+            left_op, right_op = right_op, left_op
 
     if _TEST_MODE:
         _store_test_result(result is not None)
 
     if result is None:
-        result = _evaluate_standard(op, op_str, a, b)
+        result = _evaluate_standard(op, op_str, left_op, right_op)
 
     return result
 
@@ -170,24 +170,28 @@ def _evaluate_numexpr(op, op_str, a, b):
 }
 
 
-def _where_standard(cond, a, b):
+def _where_standard(cond, left_op, right_op):
     # Caller is responsible for extracting ndarray if necessary
-    return np.where(cond, a, b)
+    return np.where(cond, left_op, right_op)
 
 
-def _where_numexpr(cond, a, b):
+def _where_numexpr(cond, left_op, right_op):
     # Caller is responsible for extracting ndarray if necessary
     result = None
 
-    if _can_use_numexpr(None, "where", a, b, "where"):
+    if _can_use_numexpr(None, "where", left_op, right_op, "where"):
         result = ne.evaluate(
-            "where(cond_value, a_value, b_value)",
-            local_dict={"cond_value": cond, "a_value": a, "b_value": b},
+            "where(cond_value, left_value, right_value)",
+            local_dict={
+                "cond_value": cond,
+                "left_value": left_op,
+                "right_value": right_op,
+            },
             casting="safe",
         )
 
     if result is None:
-        result = _where_standard(cond, a, b)
+        result = _where_standard(cond, left_op, right_op)
 
     return result
 
@@ -206,13 +210,13 @@ def _has_bool_dtype(x):
 _BOOL_OP_UNSUPPORTED = {"+": "|", "*": "&", "-": "^"}
 
 
-def _bool_arith_fallback(op_str, a, b) -> bool:
+def _bool_arith_fallback(op_str, left_op, right_op) -> bool:
     """
     Check if we should fallback to the python `_evaluate_standard` in case
     of an unsupported operation by numexpr, which is the case for some
     boolean ops.
     """
-    if _has_bool_dtype(a) and _has_bool_dtype(b):
+    if _has_bool_dtype(left_op) and _has_bool_dtype(right_op):
         if op_str in _BOOL_OP_UNSUPPORTED:
             warnings.warn(
                 f"evaluating in Python space because the {op_str!r} "
@@ -224,40 +228,44 @@ def _bool_arith_fallback(op_str, a, b) -> bool:
     return False
 
 
-def evaluate(op, a, b, use_numexpr: bool = True):
+def evaluate(op, left_op, right_op, use_numexpr: bool = True):
     """
-    Evaluate and return the expression of the op on a and b.
+    Evaluate and return the expression of the op on left_op and right_op.
 
     Parameters
     ----------
     op : the actual operand
-    a : left operand
-    b : right operand
+    left_op : left operand
+    right_op : right operand
     use_numexpr : bool, default True
         Whether to try to use numexpr.
     """
     op_str = _op_str_mapping[op]
     if op_str is not None:
         if use_numexpr:
             # error: "None" not callable
-            return _evaluate(op, op_str, a, b)  # type: ignore[misc]
-    return _evaluate_standard(op, op_str, a, b)
+            return _evaluate(op, op_str, left_op, right_op)  # type: ignore[misc]
+    return _evaluate_standard(op, op_str, left_op, right_op)
 
 
-def where(cond, a, b, use_numexpr: bool = True):
+def where(cond, left_op, right_op, use_numexpr: bool = True):
     """
-    Evaluate the where condition cond on a and b.
+    Evaluate the where condition cond on left_op and right_op.
 
     Parameters
     ----------
     cond : np.ndarray[bool]
-    a : return if cond is True
-    b : return if cond is False
+    left_op : return if cond is True
+    right_op : return if cond is False
     use_numexpr : bool, default True
         Whether to try to use numexpr.
     """
     assert _where is not None
-    return _where(cond, a, b) if use_numexpr else _where_standard(cond, a, b)
+    return (
+        _where(cond, left_op, right_op)
+        if use_numexpr
+        else _where_standard(cond, left_op, right_op)
+    )
 
 
 def set_test_mode(v: bool = True) -> None:

pandas/core/computation/pytables.py

@@ -408,11 +408,12 @@ def prune(self, klass):
         operand = operand.prune(klass)
 
         if operand is not None and (
-            issubclass(klass, ConditionBinOp)
-            and operand.condition is not None
-            or not issubclass(klass, ConditionBinOp)
-            and issubclass(klass, FilterBinOp)
-            and operand.filter is not None
+            (issubclass(klass, ConditionBinOp) and operand.condition is not None)
+            or (
+                not issubclass(klass, ConditionBinOp)
+                and issubclass(klass, FilterBinOp)
+                and operand.filter is not None
+            )
         ):
             return operand.invert()
         return None

pandas/core/computation/scope.py

@@ -140,7 +140,7 @@ class Scope:
     temps : dict
     """
 
-    __slots__ = ["level", "scope", "target", "resolvers", "temps"]
+    __slots__ = ["level", "resolvers", "scope", "target", "temps"]
     level: int
     scope: DeepChainMap
     resolvers: DeepChainMap