Error raised message: DataFrame eval will not work with 'Timestamp' column name (#47273)

Author: Khor Chean Wei

doc/source/whatsnew/v1.5.0.rst

@@ -932,6 +932,8 @@ Conversion
 - Bug in :meth:`DataFrame.to_dict` for ``orient="list"`` or ``orient="index"`` was not returning native types (:issue:`46751`)
 - Bug in :meth:`DataFrame.apply` that returns a :class:`DataFrame` instead of a :class:`Series` when applied to an empty :class:`DataFrame` and ``axis=1`` (:issue:`39111`)
 - Bug when inferring the dtype from an iterable that is *not* a NumPy ``ndarray`` consisting of all NumPy unsigned integer scalars did not result in an unsigned integer dtype (:issue:`47294`)
+- Bug in :meth:`DataFrame.eval` when pandas objects (e.g. ``'Timestamp'``) were column names (:issue:`44603`)
+-
 
 Strings
 ^^^^^^^

pandas/core/computation/ops.py

@@ -99,7 +99,14 @@ def evaluate(self, *args, **kwargs) -> Term:
         return self
 
     def _resolve_name(self):
-        res = self.env.resolve(self.local_name, is_local=self.is_local)
+        local_name = str(self.local_name)
+        is_local = self.is_local
+        if local_name in self.env.scope and isinstance(
+            self.env.scope[local_name], type
+        ):
+            is_local = False
+
+        res = self.env.resolve(local_name, is_local=is_local)
         self.update(res)
 
         if hasattr(res, "ndim") and res.ndim > 2:

pandas/tests/computation/test_eval.py

@@ -49,6 +49,7 @@
     _binary_ops_dict,
     _unary_math_ops,
 )
+from pandas.core.computation.scope import DEFAULT_GLOBALS
 
 
 @pytest.fixture(
@@ -1890,6 +1891,27 @@ def test_negate_lt_eq_le(engine, parser):
         tm.assert_frame_equal(result, expected)
 
 
+@pytest.mark.parametrize(
+    "column",
+    DEFAULT_GLOBALS.keys(),
+)
+def test_eval_no_support_column_name(request, column):
+    # GH 44603
+    if column in ["True", "False", "inf", "Inf"]:
+        request.node.add_marker(
+            pytest.mark.xfail(
+                raises=KeyError,
+                reason=f"GH 47859 DataFrame eval not supported with {column}",
+            )
+        )
+
+    df = DataFrame(np.random.randint(0, 100, size=(10, 2)), columns=[column, "col1"])
+    expected = df[df[column] > 6]
+    result = df.query(f"{column}>6")
+
+    tm.assert_frame_equal(result, expected)
+
+
 class TestValidate:
     @pytest.mark.parametrize("value", [1, "True", [1, 2, 3], 5.0])
     def test_validate_bool_args(self, value):