applied patch

pandas/core/apply.py

@@ -64,7 +64,6 @@
     from pandas.core.resample import Resampler
     from pandas.core.window.rolling import BaseWindow
 
-
 ResType = Dict[int, Any]
 
 
@@ -285,10 +284,9 @@ def transform_str_or_callable(self, func) -> DataFrame | Series:
             return func(obj, *args, **kwargs)
 
     def _filter_numeric_only(self) -> list[Any]:
-        if "numeric_only" in self.kwargs and self.kwargs["numeric_only"] is True:
+        if "numeric_only" in self.kwargs and bool(self.kwargs["numeric_only"]) is True:
             obj = self.obj._get_numeric_data()
-            filtered_cols = list(set(self.obj) - set(obj))
-            self.obj = obj
+            filtered_cols = list(obj)
             return filtered_cols
         return []
 
@@ -302,8 +300,9 @@ def agg_list_like(self) -> DataFrame | Series:
         """
         from pandas.core.reshape.concat import concat
 
-        self._filter_numeric_only()
-        obj = self.obj
+        filtered_cols = self._filter_numeric_only()
+        n = len(filtered_cols)
+        obj = self.obj if n == 0 else self.obj[filtered_cols].astype("O")
         arg = cast(List[AggFuncTypeBase], self.f)
 
         if getattr(obj, "axis", 0) == 1:
@@ -377,11 +376,8 @@ def agg_dict_like(self) -> DataFrame | Series:
         from pandas import Index
         from pandas.core.reshape.concat import concat
 
-        filtered_col = self._filter_numeric_only()
-
         obj = self.obj
         arg = cast(AggFuncTypeDict, self.f)
-        arg = {k: arg[k] for k in arg.keys() if k not in filtered_col}
 
         if getattr(obj, "axis", 0) == 1:
             raise NotImplementedError("axis other than 0 is not supported")
@@ -1181,7 +1177,6 @@ def reconstruct_func(
 
     if not relabeling:
         if isinstance(func, list) and len(func) > len(set(func)):
-
             # GH 28426 will raise error if duplicated function names are used and
             # there is no reassigned name
             raise SpecificationError(

pandas/core/frame.py

@@ -9221,10 +9221,10 @@ def _gotitem(
     ...                    ['c', 3, 6]],
     ...                   columns=['A', 'B', 'C'])
 
-    Works equivalently as above. Add argument `numeric_only=True` to avoid
-    exceptions or warnings.
+    Works equivalently as above. Add argument `numeric_only=True` to
+    aggregate only numeric columns.
 
-    >>> df.agg({'A': 'mean', 'B': [pd.DataFrame.mean, 'std'], 'C': ['sum', 'mean']},
+    >>> df.agg({'B': ['mean', 'std'], 'C': ['sum', 'mean']},
     ...                    numeric_only=True)
             B     C
     mean  2.0   5.0
@@ -9394,6 +9394,9 @@ def apply(
         Functions that mutate the passed object can produce unexpected
         behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
         for more details.
+        Use the keyword argument `numeric_only=True` to apply functions
+        only to numeric columns and to skip the non-numeric columns.
+        e.g. the column contains a string.
 
         Examples
         --------

pandas/core/shared_docs.py

@@ -46,6 +46,10 @@
 for more details.
 
 A passed user-defined-function will be passed a Series for evaluation.
+
+Use the keyword argument `numeric_only=True` to apply functions
+only to numeric columns and to skip the non-numeric columns.
+e.g. the column contains a string.
 {examples}"""
 
 _shared_docs[