REF: NDFrame dont mixin SelectionMixin (#40857)

pandas/_testing/__init__.py

@@ -918,12 +918,12 @@ def external_error_raised(expected_exception: type[Exception]) -> ContextManager
     return pytest.raises(expected_exception, match=None)
 
 
-cython_table = pd.core.base.SelectionMixin._cython_table.items()
+cython_table = pd.core.common._cython_table.items()
 
 
 def get_cython_table_params(ndframe, func_names_and_expected):
     """
-    Combine frame, functions from SelectionMixin._cython_table
+    Combine frame, functions from com._cython_table
     keys and expected result.
 
     Parameters

pandas/core/apply.py

@@ -44,6 +44,7 @@
 from pandas.core.algorithms import safe_sort
 from pandas.core.base import (
     DataError,
+    SelectionMixin,
     SpecificationError,
 )
 import pandas.core.common as com
@@ -173,7 +174,7 @@ def agg(self) -> FrameOrSeriesUnion | None:
             return self.agg_list_like()
 
         if callable(arg):
-            f = obj._get_cython_func(arg)
+            f = com.get_cython_func(arg)
             if f and not args and not kwargs:
                 return getattr(obj, f)()
 
@@ -301,10 +302,10 @@ def transform_str_or_callable(self, func) -> FrameOrSeriesUnion:
         kwargs = self.kwargs
 
         if isinstance(func, str):
-            return obj._try_aggregate_string_function(func, *args, **kwargs)
+            return self._try_aggregate_string_function(obj, func, *args, **kwargs)
 
         if not args and not kwargs:
-            f = obj._get_cython_func(func)
+            f = com.get_cython_func(func)
             if f:
                 return getattr(obj, f)()
 
@@ -327,7 +328,10 @@ def agg_list_like(self) -> FrameOrSeriesUnion:
         obj = self.obj
         arg = cast(List[AggFuncTypeBase], self.f)
 
-        if obj._selected_obj.ndim == 1:
+        if not isinstance(obj, SelectionMixin):
+            # i.e. obj is Series or DataFrame
+            selected_obj = obj
+        elif obj._selected_obj.ndim == 1:
             selected_obj = obj._selected_obj
         else:
             selected_obj = obj._obj_with_exclusions
@@ -406,13 +410,19 @@ def agg_dict_like(self) -> FrameOrSeriesUnion:
         obj = self.obj
         arg = cast(AggFuncTypeDict, self.f)
 
-        selected_obj = obj._selected_obj
+        if not isinstance(obj, SelectionMixin):
+            # i.e. obj is Series or DataFrame
+            selected_obj = obj
+            selection = None
+        else:
+            selected_obj = obj._selected_obj
+            selection = obj._selection
 
         arg = self.normalize_dictlike_arg("agg", selected_obj, arg)
 
         if selected_obj.ndim == 1:
             # key only used for output
-            colg = obj._gotitem(obj._selection, ndim=1)
+            colg = obj._gotitem(selection, ndim=1)
             results = {key: colg.agg(how) for key, how in arg.items()}
         else:
             # key used for column selection and output
@@ -486,7 +496,7 @@ def maybe_apply_str(self) -> FrameOrSeriesUnion | None:
                 self.kwargs["axis"] = self.axis
             elif self.axis != 0:
                 raise ValueError(f"Operation {f} does not support axis=1")
-        return obj._try_aggregate_string_function(f, *self.args, **self.kwargs)
+        return self._try_aggregate_string_function(obj, f, *self.args, **self.kwargs)
 
     def maybe_apply_multiple(self) -> FrameOrSeriesUnion | None:
         """
@@ -547,6 +557,35 @@ def normalize_dictlike_arg(
             func = new_func
         return func
 
+    def _try_aggregate_string_function(self, obj, arg: str, *args, **kwargs):
+        """
+        if arg is a string, then try to operate on it:
+        - try to find a function (or attribute) on ourselves
+        - try to find a numpy function
+        - raise
+        """
+        assert isinstance(arg, str)
+
+        f = getattr(obj, arg, None)
+        if f is not None:
+            if callable(f):
+                return f(*args, **kwargs)
+
+            # people may try to aggregate on a non-callable attribute
+            # but don't let them think they can pass args to it
+            assert len(args) == 0
+            assert len([kwarg for kwarg in kwargs if kwarg not in ["axis"]]) == 0
+            return f
+
+        f = getattr(np, arg, None)
+        if f is not None and hasattr(obj, "__array__"):
+            # in particular exclude Window
+            return f(obj, *args, **kwargs)
+
+        raise AttributeError(
+            f"'{arg}' is not a valid function for '{type(obj).__name__}' object"
+        )
+
 
 class FrameApply(Apply):
     obj: DataFrame

pandas/core/base.py

@@ -2,12 +2,10 @@
 Base and utility classes for pandas objects.
 """
 
-import builtins
 import textwrap
 from typing import (
     TYPE_CHECKING,
     Any,
-    Callable,
     Dict,
     FrozenSet,
     Optional,
@@ -176,36 +174,6 @@ class SelectionMixin:
     _internal_names = ["_cache", "__setstate__"]
     _internal_names_set = set(_internal_names)
 
-    _builtin_table = {builtins.sum: np.sum, builtins.max: np.max, builtins.min: np.min}
-
-    _cython_table = {
-        builtins.sum: "sum",
-        builtins.max: "max",
-        builtins.min: "min",
-        np.all: "all",
-        np.any: "any",
-        np.sum: "sum",
-        np.nansum: "sum",
-        np.mean: "mean",
-        np.nanmean: "mean",
-        np.prod: "prod",
-        np.nanprod: "prod",
-        np.std: "std",
-        np.nanstd: "std",
-        np.var: "var",
-        np.nanvar: "var",
-        np.median: "median",
-        np.nanmedian: "median",
-        np.max: "max",
-        np.nanmax: "max",
-        np.min: "min",
-        np.nanmin: "min",
-        np.cumprod: "cumprod",
-        np.nancumprod: "cumprod",
-        np.cumsum: "cumsum",
-        np.nancumsum: "cumsum",
-    }
-
     @property
     def _selection_name(self):
         """
@@ -216,6 +184,7 @@ def _selection_name(self):
         """
         return self._selection
 
+    @final
     @property
     def _selection_list(self):
         if not isinstance(
@@ -240,6 +209,7 @@ def _selected_obj(self):
     def ndim(self) -> int:
         return self._selected_obj.ndim
 
+    @final
     @cache_readonly
     def _obj_with_exclusions(self):
         # error: "SelectionMixin" has no attribute "obj"
@@ -308,48 +278,6 @@ def aggregate(self, func, *args, **kwargs):
 
     agg = aggregate
 
-    def _try_aggregate_string_function(self, arg: str, *args, **kwargs):
-        """
-        if arg is a string, then try to operate on it:
-        - try to find a function (or attribute) on ourselves
-        - try to find a numpy function
-        - raise
-        """
-        assert isinstance(arg, str)
-
-        f = getattr(self, arg, None)
-        if f is not None:
-            if callable(f):
-                return f(*args, **kwargs)
-
-            # people may try to aggregate on a non-callable attribute
-            # but don't let them think they can pass args to it
-            assert len(args) == 0
-            assert len([kwarg for kwarg in kwargs if kwarg not in ["axis"]]) == 0
-            return f
-
-        f = getattr(np, arg, None)
-        if f is not None and hasattr(self, "__array__"):
-            # in particular exclude Window
-            return f(self, *args, **kwargs)
-
-        raise AttributeError(
-            f"'{arg}' is not a valid function for '{type(self).__name__}' object"
-        )
-
-    def _get_cython_func(self, arg: Callable) -> Optional[str]:
-        """
-        if we define an internal function for this argument, return it
-        """
-        return self._cython_table.get(arg)
-
-    def _is_builtin_func(self, arg):
-        """
-        if we define an builtin function for this argument, return it,
-        otherwise return the arg
-        """
-        return self._builtin_table.get(arg, arg)
-
 
 class IndexOpsMixin(OpsMixin):
     """

pandas/core/common.py

@@ -5,6 +5,7 @@
 """
 from __future__ import annotations
 
+import builtins
 from collections import (
     abc,
     defaultdict,
@@ -532,3 +533,49 @@ def require_length_match(data, index: Index):
             "does not match length of index "
             f"({len(index)})"
         )
+
+
+_builtin_table = {builtins.sum: np.sum, builtins.max: np.max, builtins.min: np.min}
+
+_cython_table = {
+    builtins.sum: "sum",
+    builtins.max: "max",
+    builtins.min: "min",
+    np.all: "all",
+    np.any: "any",
+    np.sum: "sum",
+    np.nansum: "sum",
+    np.mean: "mean",
+    np.nanmean: "mean",
+    np.prod: "prod",
+    np.nanprod: "prod",
+    np.std: "std",
+    np.nanstd: "std",
+    np.var: "var",
+    np.nanvar: "var",
+    np.median: "median",
+    np.nanmedian: "median",
+    np.max: "max",
+    np.nanmax: "max",
+    np.min: "min",
+    np.nanmin: "min",
+    np.cumprod: "cumprod",
+    np.nancumprod: "cumprod",
+    np.cumsum: "cumsum",
+    np.nancumsum: "cumsum",
+}
+
+
+def get_cython_func(arg: Callable) -> str | None:
+    """
+    if we define an internal function for this argument, return it
+    """
+    return _cython_table.get(arg)
+
+
+def is_builtin_func(arg):
+    """
+    if we define an builtin function for this argument, return it,
+    otherwise return the arg
+    """
+    return _builtin_table.get(arg, arg)

pandas/core/generic.py

@@ -109,10 +109,7 @@
 )
 import pandas.core.algorithms as algos
 from pandas.core.arrays import ExtensionArray
-from pandas.core.base import (
-    PandasObject,
-    SelectionMixin,
-)
+from pandas.core.base import PandasObject
 import pandas.core.common as com
 from pandas.core.construction import (
     create_series_with_explicit_dtype,
@@ -187,7 +184,7 @@
 bool_t = bool  # Need alias because NDFrame has def bool:
 
 
-class NDFrame(PandasObject, SelectionMixin, indexing.IndexingMixin):
+class NDFrame(PandasObject, indexing.IndexingMixin):
     """
     N-dimensional analogue of DataFrame. Store multi-dimensional in a
     size-mutable, labeled data structure
@@ -684,18 +681,6 @@ def size(self) -> int:
         # error: Incompatible return value type (got "number", expected "int")
         return np.prod(self.shape)  # type: ignore[return-value]
 
-    @final
-    @property
-    def _selected_obj(self: FrameOrSeries) -> FrameOrSeries:
-        """ internal compat with SelectionMixin """
-        return self
-
-    @final
-    @property
-    def _obj_with_exclusions(self: FrameOrSeries) -> FrameOrSeries:
-        """ internal compat with SelectionMixin """
-        return self
-
     @overload
     def set_axis(
         self: FrameOrSeries, labels, axis: Axis = ..., inplace: Literal[False] = ...

pandas/core/groupby/generic.py

@@ -268,7 +268,7 @@ def aggregate(self, func=None, *args, engine=None, engine_kwargs=None, **kwargs)
             if relabeling:
                 ret.columns = columns
         else:
-            cyfunc = self._get_cython_func(func)
+            cyfunc = com.get_cython_func(func)
             if cyfunc and not args and not kwargs:
                 return getattr(self, cyfunc)()
 
@@ -536,7 +536,7 @@ def transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
                 result.ravel(), index=data.index, name=data.name
             )
 
-        func = self._get_cython_func(func) or func
+        func = com.get_cython_func(func) or func
 
         if not isinstance(func, str):
             return self._transform_general(func, *args, **kwargs)
@@ -1440,7 +1440,7 @@ def transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
             return self.obj._constructor(result, index=data.index, columns=data.columns)
 
         # optimized transforms
-        func = self._get_cython_func(func) or func
+        func = com.get_cython_func(func) or func
 
         if not isinstance(func, str):
             return self._transform_general(func, *args, **kwargs)

pandas/core/groupby/groupby.py

@@ -909,7 +909,7 @@ def __iter__(self) -> Iterator[tuple[Hashable, FrameOrSeries]]:
     )
     def apply(self, func, *args, **kwargs):
 
-        func = self._is_builtin_func(func)
+        func = com.is_builtin_func(func)
 
         # this is needed so we don't try and wrap strings. If we could
         # resolve functions to their callable functions prior, this
@@ -1205,7 +1205,7 @@ def _aggregate_with_numba(self, data, func, *args, engine_kwargs=None, **kwargs)
 
     @final
     def _python_agg_general(self, func, *args, **kwargs):
-        func = self._is_builtin_func(func)
+        func = com.is_builtin_func(func)
         f = lambda x: func(x, *args, **kwargs)
 
         # iterate through "columns" ex exclusions to populate output dict