Added test for covariance

Author: jhendricks487

.github/CODEOWNERS

@@ -10,10 +10,8 @@ doc/source/development            @noatamir
 
 # pandas
 pandas/_libs/                     @WillAyd
-pandas/_libs/tslibs/*             @MarcoGorelli
 pandas/_typing.py                 @Dr-Irv
 pandas/core/groupby/*             @rhshadrach
-pandas/core/tools/datetimes.py    @MarcoGorelli
 pandas/io/excel/*                 @rhshadrach
 pandas/io/formats/style.py        @attack68
 pandas/io/formats/style_render.py @attack68

.github/workflows/wheels.yml

@@ -153,7 +153,7 @@ jobs:
         run: echo "sdist_name=$(cd ./dist && ls -d */)" >> "$GITHUB_ENV"
 
       - name: Build wheels
-        uses: pypa/[email protected].1
+        uses: pypa/[email protected].2
         with:
          package-dir: ./dist/${{ startsWith(matrix.buildplat[1], 'macosx') && env.sdist_name || needs.build_sdist.outputs.sdist_file }}
         env:

ci/meta.yaml

@@ -89,4 +89,3 @@ extra:
     - datapythonista
     - phofl
     - lithomas1
-    - marcogorelli

doc/source/development/debugging_extensions.rst

@@ -23,7 +23,7 @@ By default building pandas from source will generate a release build. To generat
 
 .. note::
 
-   conda environments update CFLAGS/CPPFLAGS with flags that are geared towards generating releases. If using conda, you may need to set ``CFLAGS="$CFLAGS -O0"`` and ``CPPFLAGS="$CPPFLAGS -O0"`` to ensure optimizations are turned off for debugging
+   conda environments update CFLAGS/CPPFLAGS with flags that are geared towards generating releases, and may work counter towards usage in a development environment. If using conda, you should unset these environment variables via ``export CFLAGS=`` and ``export CPPFLAGS=``
 
 By specifying ``builddir="debug"`` all of the targets will be built and placed in the debug directory relative to the project root. This helps to keep your debug and release artifacts separate; you are of course able to choose a different directory name or omit altogether if you do not care to separate build types.
 

doc/source/whatsnew/v3.0.0.rst

@@ -825,6 +825,7 @@ Other
 - Bug in :class:`DataFrame` when passing a ``dict`` with a NA scalar and ``columns`` that would always return ``np.nan`` (:issue:`57205`)
 - Bug in :class:`Series` ignoring errors when trying to convert :class:`Series` input data to the given ``dtype`` (:issue:`60728`)
 - Bug in :func:`eval` on :class:`ExtensionArray` on including division ``/`` failed with a ``TypeError``. (:issue:`58748`)
+- Bug in :func:`eval` where method calls on binary operations like ``(x + y).dropna()`` would raise ``AttributeError: 'BinOp' object has no attribute 'value'`` (:issue:`61175`)
 - Bug in :func:`eval` where the names of the :class:`Series` were not preserved when using ``engine="numexpr"``. (:issue:`10239`)
 - Bug in :func:`eval` with ``engine="numexpr"`` returning unexpected result for float division. (:issue:`59736`)
 - Bug in :func:`to_numeric` raising ``TypeError`` when ``arg`` is a :class:`Timedelta` or :class:`Timestamp` scalar. (:issue:`59944`)
@@ -834,6 +835,7 @@ Other
 - Bug in :meth:`DataFrame.eval` and :meth:`DataFrame.query` which did not allow to use ``tan`` function. (:issue:`55091`)
 - Bug in :meth:`DataFrame.query` where using duplicate column names led to a ``TypeError``. (:issue:`59950`)
 - Bug in :meth:`DataFrame.query` which raised an exception or produced incorrect results when expressions contained backtick-quoted column names containing the hash character ``#``, backticks, or characters that fall outside the ASCII range (U+0001..U+007F). (:issue:`59285`) (:issue:`49633`)
+- Bug in :meth:`DataFrame.query` which raised an exception when querying integer column names using backticks. (:issue:`60494`)
 - Bug in :meth:`DataFrame.shift` where passing a ``freq`` on a DataFrame with no columns did not shift the index correctly. (:issue:`60102`)
 - Bug in :meth:`DataFrame.sort_index` when passing ``axis="columns"`` and ``ignore_index=True`` and ``ascending=False`` not returning a :class:`RangeIndex` columns (:issue:`57293`)
 - Bug in :meth:`DataFrame.transform` that was returning the wrong order unless the index was monotonically increasing. (:issue:`57069`)

pandas/core/computation/expr.py

@@ -644,7 +644,11 @@ def visit_Attribute(self, node, **kwargs):
         ctx = node.ctx
         if isinstance(ctx, ast.Load):
             # resolve the value
-            resolved = self.visit(value).value
+            visited_value = self.visit(value)
+            if hasattr(visited_value, "value"):
+                resolved = visited_value.value
+            else:
+                resolved = visited_value(self.env)
             try:
                 v = getattr(resolved, attr)
                 name = self.env.add_tmp(v)

pandas/core/generic.py

@@ -612,7 +612,6 @@ def _get_cleaned_column_resolvers(self) -> dict[Hashable, Series]:
                 v, copy=False, index=self.index, name=k, dtype=dtype
             ).__finalize__(self)
             for k, v, dtype in zip(self.columns, self._iter_column_arrays(), dtypes)
-            if not isinstance(k, int)
         }
 
     @final

pandas/core/missing.py

@@ -312,18 +312,9 @@ def get_interp_index(method, index: Index) -> Index:
     # create/use the index
     if method == "linear":
         # prior default
-        from pandas import Index
-
-        if isinstance(index.dtype, DatetimeTZDtype) or lib.is_np_dtype(
-            index.dtype, "mM"
-        ):
-            # Convert datetime-like indexes to int64
-            index = Index(index.view("i8"))
-
-        elif not is_numeric_dtype(index.dtype):
-            # We keep behavior consistent with prior versions of pandas for
-            # non-numeric, non-datetime indexes
-            index = Index(range(len(index)))
+        from pandas import RangeIndex
+
+        index = RangeIndex(len(index))
     else:
         methods = {"index", "values", "nearest", "time"}
         is_numeric_or_datetime = (

pandas/core/resample.py

@@ -897,17 +897,17 @@ def interpolate(
         to non-aligned timestamps, as in the following example:
 
         >>> series.resample("400ms").interpolate("linear")
-        2023-03-01 07:00:00.000    1.0
-        2023-03-01 07:00:00.400    0.2
-        2023-03-01 07:00:00.800   -0.6
-        2023-03-01 07:00:01.200   -0.4
-        2023-03-01 07:00:01.600    0.8
-        2023-03-01 07:00:02.000    2.0
-        2023-03-01 07:00:02.400    1.6
-        2023-03-01 07:00:02.800    1.2
-        2023-03-01 07:00:03.200    1.4
-        2023-03-01 07:00:03.600    2.2
-        2023-03-01 07:00:04.000    3.0
+        2023-03-01 07:00:00.000    1.000000
+        2023-03-01 07:00:00.400    0.333333
+        2023-03-01 07:00:00.800   -0.333333
+        2023-03-01 07:00:01.200    0.000000
+        2023-03-01 07:00:01.600    1.000000
+        2023-03-01 07:00:02.000    2.000000
+        2023-03-01 07:00:02.400    1.666667
+        2023-03-01 07:00:02.800    1.333333
+        2023-03-01 07:00:03.200    1.666667
+        2023-03-01 07:00:03.600    2.333333
+        2023-03-01 07:00:04.000    3.000000
         Freq: 400ms, dtype: float64
 
         Note that the series correctly decreases between two anchors

pandas/tests/computation/test_eval.py

@@ -2006,3 +2006,24 @@ def test_eval_float_div_numexpr():
     result = pd.eval("1 / 2", engine="numexpr")
     expected = 0.5
     assert result == expected
+
+
+def test_method_calls_on_binop():
+    # GH 61175
+    x = Series([1, 2, 3, 5])
+    y = Series([2, 3, 4])
+
+    # Method call on binary operation result
+    result = pd.eval("(x + y).dropna()")
+    expected = (x + y).dropna()
+    tm.assert_series_equal(result, expected)
+
+    # Test with other binary operations
+    result = pd.eval("(x * y).dropna()")
+    expected = (x * y).dropna()
+    tm.assert_series_equal(result, expected)
+
+    # Test with method chaining
+    result = pd.eval("(x + y).dropna().reset_index(drop=True)")
+    expected = (x + y).dropna().reset_index(drop=True)
+    tm.assert_series_equal(result, expected)