BUG: Validate numeric_only parameter in groupby aggregations

pandas/core/groupby/groupby.py

@@ -1754,44 +1754,48 @@ def _cython_agg_general(
         **kwargs,
     ):
         # Note: we never get here with how="ohlc" for DataFrameGroupBy;
-        #  that goes through SeriesGroupBy
+        # that goes through SeriesGroupBy
 
-        data = self._get_data_to_aggregate(numeric_only=numeric_only, name=how)
+        # Check to confirm numeric_only is fed either True or False and no other data type
+        if(isinstance(numeric_only, bool)):
+            data = self._get_data_to_aggregate(numeric_only=numeric_only, name=how)
 
-        def array_func(values: ArrayLike) -> ArrayLike:
-            try:
-                result = self._grouper._cython_operation(
-                    "aggregate",
-                    values,
-                    how,
-                    axis=data.ndim - 1,
-                    min_count=min_count,
-                    **kwargs,
-                )
-            except NotImplementedError:
-                # generally if we have numeric_only=False
-                # and non-applicable functions
-                # try to python agg
-                # TODO: shouldn't min_count matter?
-                # TODO: avoid special casing SparseArray here
-                if how in ["any", "all"] and isinstance(values, SparseArray):
-                    pass
-                elif alt is None or how in ["any", "all", "std", "sem"]:
-                    raise  # TODO: re-raise as TypeError?  should not be reached
-            else:
-                return result
+            def array_func(values: ArrayLike) -> ArrayLike:
+                try:
+                    result = self._grouper._cython_operation(
+                        "aggregate",
+                        values,
+                        how,
+                        axis=data.ndim - 1,
+                        min_count=min_count,
+                        **kwargs,
+                    )
+                except NotImplementedError:
+                    # generally if we have numeric_only=False
+                    # and non-applicable functions
+                    # try to python agg
+                    # TODO: shouldn't min_count matter?
+                    # TODO: avoid special casing SparseArray here
+                    if how in ["any", "all"] and isinstance(values, SparseArray):
+                        pass
+                    elif alt is None or how in ["any", "all", "std", "sem"]:
+                        raise  # TODO: re-raise as TypeError?  should not be reached
+                else:
+                    return result
 
-            assert alt is not None
-            result = self._agg_py_fallback(how, values, ndim=data.ndim, alt=alt)
-            return result
+                assert alt is not None
+                result = self._agg_py_fallback(how, values, ndim=data.ndim, alt=alt)
+                return result
 
-        new_mgr = data.grouped_reduce(array_func)
-        res = self._wrap_agged_manager(new_mgr)
-        if how in ["idxmin", "idxmax"]:
-            # mypy expects how to be Literal["idxmin", "idxmax"].
-            res = self._wrap_idxmax_idxmin(res, how=how, skipna=kwargs["skipna"])  # type: ignore[arg-type]
-        out = self._wrap_aggregated_output(res)
-        return out
+            new_mgr = data.grouped_reduce(array_func)
+            res = self._wrap_agged_manager(new_mgr)
+            if how in ["idxmin", "idxmax"]:
+                # mypy expects how to be Literal["idxmin", "idxmax"].
+                res = self._wrap_idxmax_idxmin(res, how=how, skipna=kwargs["skipna"])  # type: ignore[arg-type]
+            out = self._wrap_aggregated_output(res)
+            return out
+        else:
+            raise ValueError("numeric_only accepts only Boolean values")
 
     def _cython_transform(self, how: str, numeric_only: bool = False, **kwargs):
         raise AbstractMethodError(self)

pandas/tests/groupby/test_reductions.py

@@ -1520,3 +1520,27 @@ def test_groupby_std_datetimelike():
     exp_ser = Series([td1 * 2, td1, td1, td1, td4], index=np.arange(5))
     expected = DataFrame({"A": exp_ser, "B": exp_ser, "C": exp_ser})
     tm.assert_frame_equal(result, expected)
+
+def test_mean_numeric_only_validates_bool():
+    """
+    Test that numeric_only parameter only accepts boolean values.
+    See GH#62778
+    """
+    df = pd.DataFrame({"A": range(5), "B": range(5)})
+
+    # These test cases should raise a ValueError
+    msg = "numeric_only accepts only Boolean values"
+    with pytest.raises(ValueError, match=msg):
+        df.groupby(["A"]).mean(["B"])
+
+    with pytest.raises(ValueError, match=msg):
+        df.groupby(["A"]).mean(numeric_only="True")
+
+    with pytest.raises(ValueError, match=msg):
+        df.groupby(["A"]).mean(numeric_only=1)
+
+    # These test cases should work absolutely fine
+    df.groupby(["A"]).mean()
+    df.groupby(["A"]).mean(numeric_only=True)
+    df.groupby(["A"]).mean(numeric_only=False)
+