DataFrame.eval error fixed

Author: MdRiyazulIslam

pandas/core/computation/align.py

@@ -1,20 +1,20 @@
 """
 Core eval alignment algorithms.
 """
-
 from __future__ import annotations
 
 from functools import (
     partial,
     wraps,
 )
-from typing import TYPE_CHECKING
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+)
 import warnings
 
 import numpy as np
 
-from pandas._config.config import get_option
-
 from pandas.errors import PerformanceWarning
 from pandas.util._exceptions import find_stack_level
 
@@ -28,10 +28,7 @@
 from pandas.core.computation.common import result_type_many
 
 if TYPE_CHECKING:
-    from collections.abc import (
-        Callable,
-        Sequence,
-    )
+    from collections.abc import Sequence
 
     from pandas._typing import F
 
@@ -113,7 +110,7 @@ def _align_core(terms):
                 ax, itm = axis, items
 
             if not axes[ax].is_(itm):
-                axes[ax] = axes[ax].union(itm)
+                axes[ax] = axes[ax].join(itm, how="outer")
 
     for i, ndim in ndims.items():
         for axis, items in zip(range(ndim), axes):
@@ -127,24 +124,20 @@ def _align_core(terms):
                 reindexer_size = len(reindexer)
 
                 ordm = np.log10(max(1, abs(reindexer_size - term_axis_size)))
-                if (
-                    get_option("performance_warnings")
-                    and ordm >= 1
-                    and reindexer_size >= 10000
-                ):
+                if ordm >= 1 and reindexer_size >= 10000:
                     w = (
                         f"Alignment difference on axis {axis} is larger "
-                        f"than an order of magnitude on term {terms[i].name!r}, "
+                        f"than an order of magnitude on term {repr(terms[i].name)}, "
                         f"by more than {ordm:.4g}; performance may suffer."
                     )
                     warnings.warn(
                         w, category=PerformanceWarning, stacklevel=find_stack_level()
                     )
 
-                obj = ti.reindex(reindexer, axis=axis)
+                obj = ti.reindex(reindexer, axis=axis, copy=False)
                 terms[i].update(obj)
 
-        terms[i].update(terms[i].value.values)
+        terms[i].update(terms[i].value)
 
     return typ, _zip_axes_from_type(typ, axes)
 
@@ -160,24 +153,19 @@ def align_terms(terms):
         # can't iterate so it must just be a constant or single variable
         if isinstance(terms.value, (ABCSeries, ABCDataFrame)):
             typ = type(terms.value)
-            name = terms.value.name if isinstance(terms.value, ABCSeries) else None
-            return typ, _zip_axes_from_type(typ, terms.value.axes), name
-        return np.result_type(terms.type), None, None
+            return typ, _zip_axes_from_type(typ, terms.value.axes)
+        return np.result_type(terms.type), None
 
     # if all resolved variables are numeric scalars
     if all(term.is_scalar for term in terms):
-        return result_type_many(*(term.value for term in terms)).type, None, None
-
-    # if all input series have a common name, propagate it to the returned series
-    names = {term.value.name for term in terms if isinstance(term.value, ABCSeries)}
-    name = names.pop() if len(names) == 1 else None
+        return result_type_many(*(term.value for term in terms)).type, None
 
     # perform the main alignment
     typ, axes = _align_core(terms)
-    return typ, axes, name
+    return typ, axes
 
 
-def reconstruct_object(typ, obj, axes, dtype, name):
+def reconstruct_object(typ, obj, axes, dtype):
     """
     Reconstruct an object given its type, raw value, and possibly empty
     (None) axes.
@@ -194,8 +182,8 @@ def reconstruct_object(typ, obj, axes, dtype, name):
     Returns
     -------
     ret : typ
-        An object of type ``typ`` with the value `obj` and possible axes
-        `axes`.
+        An object of type typ with the value obj and possible axes
+        axes.
     """
     try:
         typ = typ.type
@@ -205,9 +193,7 @@ def reconstruct_object(typ, obj, axes, dtype, name):
     res_t = np.result_type(obj.dtype, dtype)
 
     if not isinstance(typ, partial) and issubclass(typ, PandasObject):
-        if name is None:
-            return typ(obj, dtype=res_t, **axes)
-        return typ(obj, dtype=res_t, name=name, **axes)
+        return typ(obj, dtype=res_t, **axes)
 
     # special case for pathological things like ~True/~False
     if hasattr(res_t, "type") and typ == np.bool_ and res_t != np.bool_: