Merge branch 'master' of https://github.com/pandas-dev/pandas into re…

…f-blockwise-3

doc/source/whatsnew/v1.1.2.rst

@@ -24,7 +24,7 @@ Fixed regressions
 
 Bug fixes
 ~~~~~~~~~
-
+- Bug in :meth:`DataFrame.eval` with ``object`` dtype column binary operations (:issue:`35794`)
 -
 -
 

pandas/core/computation/ops.py

@@ -481,13 +481,21 @@ def stringify(value):
             self.lhs.update(v)
 
     def _disallow_scalar_only_bool_ops(self):
+        rhs = self.rhs
+        lhs = self.lhs
+
+        # GH#24883 unwrap dtype if necessary to ensure we have a type object
+        rhs_rt = rhs.return_type
+        rhs_rt = getattr(rhs_rt, "type", rhs_rt)
+        lhs_rt = lhs.return_type
+        lhs_rt = getattr(lhs_rt, "type", lhs_rt)
         if (
-            (self.lhs.is_scalar or self.rhs.is_scalar)
+            (lhs.is_scalar or rhs.is_scalar)
             and self.op in _bool_ops_dict
             and (
                 not (
-                    issubclass(self.rhs.return_type, (bool, np.bool_))
-                    and issubclass(self.lhs.return_type, (bool, np.bool_))
+                    issubclass(rhs_rt, (bool, np.bool_))
+                    and issubclass(lhs_rt, (bool, np.bool_))
                 )
             )
         ):

pandas/core/window/rolling.py

@@ -12,7 +12,7 @@
 
 from pandas._libs.tslibs import BaseOffset, to_offset
 import pandas._libs.window.aggregations as window_aggregations
-from pandas._typing import ArrayLike, Axis, FrameOrSeries, Scalar
+from pandas._typing import ArrayLike, Axis, FrameOrSeries, Label
 from pandas.compat._optional import import_optional_dependency
 from pandas.compat.numpy import function as nv
 from pandas.util._decorators import Appender, Substitution, cache_readonly, doc
@@ -381,21 +381,31 @@ def _wrap_result(self, result, block=None, obj=None):
             return type(obj)(result, index=index, columns=block.columns)
         return result
 
-    def _wrap_results(self, results, blocks, obj, exclude=None) -> FrameOrSeries:
+    def _wrap_results(self, results, obj, skipped: List[int]) -> FrameOrSeries:
         """
         Wrap the results.
 
         Parameters
         ----------
         results : list of ndarrays
-        blocks : list of blocks
         obj : conformed data (may be resampled)
-        exclude: list of columns to exclude, default to None
+        skipped: List[int]
+            Indices of blocks that are skipped.
         """
         from pandas import Series, concat
 
+        exclude: List[Label] = []
+        if obj.ndim == 2:
+            orig_blocks = list(obj._to_dict_of_blocks(copy=False).values())
+            for i in skipped:
+                exclude.extend(orig_blocks[i].columns)
+        else:
+            orig_blocks = [obj]
+
+        kept_blocks = [blk for i, blk in enumerate(orig_blocks) if i not in skipped]
+
         final = []
-        for result, block in zip(results, blocks):
+        for result, block in zip(results, kept_blocks):
 
             result = self._wrap_result(result, block=block, obj=obj)
             if result.ndim == 1:
@@ -491,24 +501,21 @@ def _apply_blockwise(
 
         skipped: List[int] = []
         results: List[ArrayLike] = []
-        exclude: List[Scalar] = []
         for i, b in enumerate(blocks):
             try:
                 values = self._prep_values(b.values)
 
             except (TypeError, NotImplementedError) as err:
                 if isinstance(obj, ABCDataFrame):
                     skipped.append(i)
-                    exclude.extend(b.columns)
                     continue
                 else:
                     raise DataError("No numeric types to aggregate") from err
 
             result = homogeneous_func(values)
             results.append(result)
 
-        block_list = [blk for i, blk in enumerate(blocks) if i not in skipped]
-        return self._wrap_results(results, block_list, obj, exclude)
+        return self._wrap_results(results, obj, skipped)
 
     def _apply(
         self,
@@ -1283,7 +1290,7 @@ def count(self):
             ).sum()
             results.append(result)
 
-        return self._wrap_results(results, blocks, obj)
+        return self._wrap_results(results, obj, skipped=[])
 
     _shared_docs["apply"] = dedent(
         r"""

pandas/tests/frame/test_query_eval.py

@@ -160,6 +160,13 @@ 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_object_dtype_binop(self):
+        # GH#24883
+        df = pd.DataFrame({"a1": ["Y", "N"]})
+        res = df.eval("c = ((a1 == 'Y') & True)")
+        expected = pd.DataFrame({"a1": ["Y", "N"], "c": [True, False]})
+        tm.assert_frame_equal(res, expected)
+
 
 class TestDataFrameQueryWithMultiIndex:
     def test_query_with_named_multiindex(self, parser, engine):