BUG: allow use of both default+input resolvers in df.eval, GH34966

doc/source/whatsnew/v1.4.0.rst

@@ -1000,6 +1000,7 @@ Other
 - Bug in :meth:`DataFrame.diff` when passing a NumPy integer object instead of an ``int`` object (:issue:`44572`)
 - Bug in :meth:`Series.replace` raising ``ValueError`` when using ``regex=True`` with a :class:`Series` containing ``np.nan`` values (:issue:`43344`)
 - Bug in :meth:`DataFrame.to_records` where an incorrect ``n`` was used when missing names were replaced by ``level_n`` (:issue:`44818`)
+- Bug in :meth:`DataFrame.eval` where ``resolvers`` argument was overriding the default resolvers (:issue:`34966`)
 
 .. ***DO NOT USE THIS SECTION***
 

pandas/core/frame.py

@@ -4224,15 +4224,13 @@ def eval(self, expr: str, inplace: bool = False, **kwargs):
         from pandas.core.computation.eval import eval as _eval
 
         inplace = validate_bool_kwarg(inplace, "inplace")
-        resolvers = kwargs.pop("resolvers", None)
         kwargs["level"] = kwargs.pop("level", 0) + 1
-        if resolvers is None:
-            index_resolvers = self._get_index_resolvers()
-            column_resolvers = self._get_cleaned_column_resolvers()
-            resolvers = column_resolvers, index_resolvers
+        index_resolvers = self._get_index_resolvers()
+        column_resolvers = self._get_cleaned_column_resolvers()
+        resolvers = column_resolvers, index_resolvers
         if "target" not in kwargs:
             kwargs["target"] = self
-        kwargs["resolvers"] = kwargs.get("resolvers", ()) + tuple(resolvers)
+        kwargs["resolvers"] = tuple(kwargs.get("resolvers", ())) + resolvers
 
         return _eval(expr, inplace=inplace, **kwargs)
 

pandas/tests/frame/test_query_eval.py

@@ -165,6 +165,17 @@ def test_eval_resolvers_as_list(self):
         assert df.eval("a + b", resolvers=[dict1, dict2]) == dict1["a"] + dict2["b"]
         assert pd.eval("a + b", resolvers=[dict1, dict2]) == dict1["a"] + dict2["b"]
 
+    def test_eval_resolvers_combined(self):
+        # GH 34966
+        df = DataFrame(np.random.randn(10, 2), columns=list("ab"))
+        dict1 = {"c": 2}
+
+        # Both input and default index/column resolvers should be usable
+        result = df.eval("a + b * c", resolvers=[dict1])
+
+        expected = df["a"] + df["b"] * dict1["c"]
+        tm.assert_series_equal(result, expected)
+
     def test_eval_object_dtype_binop(self):
         # GH#24883
         df = DataFrame({"a1": ["Y", "N"]})