Fix/53098 numeric only disallow non bools

doc/source/whatsnew/v2.1.0.rst

@@ -518,6 +518,7 @@ Other Deprecations
 - Deprecated ``axis=1`` in :meth:`DataFrame.ewm`, :meth:`DataFrame.rolling`, :meth:`DataFrame.expanding`, transpose before calling the method instead (:issue:`51778`)
 - Deprecated ``axis=1`` in :meth:`DataFrame.groupby` and in :class:`Grouper` constructor, do ``frame.T.groupby(...)`` instead (:issue:`51203`)
 - Deprecated ``broadcast_axis`` keyword in :meth:`Series.align` and :meth:`DataFrame.align`, upcast before calling ``align`` with ``left = DataFrame({col: left for col in right.columns}, index=right.index)`` (:issue:`51856`)
+ - Deprecated passing ``numeric_only=None`` to reduction APIs (DataFrame/Series/GroupBy/Window). During the 2.x cycle pandas will emit a ``pandas.errors.PandasFutureWarning`` when ``numeric_only=None`` is used; in a future release this will raise a ``ValueError``. Please pass ``True`` or ``False`` explicitly (:issue:`53098`).
 - Deprecated ``downcast`` keyword in :meth:`Index.fillna` (:issue:`53956`)
 - Deprecated ``fill_method`` and ``limit`` keywords in :meth:`DataFrame.pct_change`, :meth:`Series.pct_change`, :meth:`.DataFrameGroupBy.pct_change`, and :meth:`.SeriesGroupBy.pct_change`, explicitly call e.g. :meth:`DataFrame.ffill` or :meth:`DataFrame.bfill` before calling ``pct_change`` instead (:issue:`53491`)
 - Deprecated ``method``, ``limit``, and ``fill_axis`` keywords in :meth:`DataFrame.align` and :meth:`Series.align`, explicitly call :meth:`DataFrame.fillna` or :meth:`Series.fillna` on the alignment results instead (:issue:`51856`)

pandas/core/frame.py

@@ -11926,6 +11926,18 @@ def _reduce(
         if axis is not None:
             axis = self._get_axis_number(axis)
 
+        # validate numeric_only is strictly a bool (disallow None, ints, etc.)
+        # Deprecate passing None to numeric_only: warn now, error in a future
+        # release. See GH#53098.
+        from pandas.util._validators import deprecate_numeric_only_none
+
+        deprecate_numeric_only_none(numeric_only, "numeric_only")
+        # During the 2.x cycle we warn if numeric_only is None but continue to
+        # accept None; later releases should set none_allowed=False to raise.
+        validate_bool_kwarg(
+            numeric_only, "numeric_only", none_allowed=True, int_allowed=False
+        )
+
         def func(values: np.ndarray):
             # We only use this in the case that operates on self.values
             return op(values, axis=axis, skipna=skipna, **kwds)

pandas/core/groupby/groupby.py

@@ -70,6 +70,7 @@ class providing the base-class of operations.
     doc,
 )
 from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import validate_bool_kwarg
 
 from pandas.core.dtypes.cast import (
     coerce_indexer_dtype,
@@ -1756,6 +1757,16 @@ def _cython_agg_general(
         # Note: we never get here with how="ohlc" for DataFrameGroupBy;
         #  that goes through SeriesGroupBy
 
+        # validate numeric_only is strictly a bool (disallow None, ints, etc.)
+        # Deprecate passing None to numeric_only: warn now, error in a future
+        # release. See GH#53098.
+        from pandas.util._validators import deprecate_numeric_only_none
+
+        deprecate_numeric_only_none(numeric_only, "numeric_only")
+        validate_bool_kwarg(
+            numeric_only, "numeric_only", none_allowed=True, int_allowed=False
+        )
+
         data = self._get_data_to_aggregate(numeric_only=numeric_only, name=how)
 
         def array_func(values: ArrayLike) -> ArrayLike:

pandas/core/series.py

@@ -6664,6 +6664,16 @@ def _reduce(
         if axis is not None:
             self._get_axis_number(axis)
 
+        # validate numeric_only is strictly a bool (disallow None, ints, etc.)
+        # Deprecate passing None to numeric_only: warn now, error in a future
+        # release. See GH#53098.
+        from pandas.util._validators import deprecate_numeric_only_none
+
+        deprecate_numeric_only_none(numeric_only, "numeric_only")
+        validate_bool_kwarg(
+            numeric_only, "numeric_only", none_allowed=True, int_allowed=False
+        )
+
         if isinstance(delegate, ExtensionArray):
             # dispatch to ExtensionArray interface
             return delegate._reduce(name, skipna=skipna, **kwds)

pandas/core/window/rolling.py

@@ -35,6 +35,7 @@
     Substitution,
     doc,
 )
+from pandas.util._validators import validate_bool_kwarg
 
 from pandas.core.dtypes.common import (
     ensure_float64,
@@ -254,6 +255,16 @@ def _validate_numeric_only(self, name: str, numeric_only: bool) -> None:
         numeric_only : bool
             Value passed by user.
         """
+        # validate numeric_only argument type
+        # Deprecate passing None to numeric_only: warn now, error in a future
+        # release. See GH#53098.
+        from pandas.util._validators import deprecate_numeric_only_none
+
+        deprecate_numeric_only_none(numeric_only, "numeric_only")
+        validate_bool_kwarg(
+            numeric_only, "numeric_only", none_allowed=True, int_allowed=False
+        )
+
         if (
             self._selected_obj.ndim == 1
             and numeric_only

pandas/tests/frame/test_reductions.py

@@ -850,6 +850,41 @@ def test_axis_1_sum_na(self, string_dtype_no_object, skipna, min_count):
     def test_sum_prod_nanops(self, method, unit, numeric_only):
         idx = ["a", "b", "c"]
         df = DataFrame({"a": [unit, unit], "b": [unit, np.nan], "c": [np.nan, np.nan]})
+        # New behavior: numeric_only=None is deprecated; emit a warning but
+        # continue to accept it during the deprecation period.
+        if numeric_only is None:
+            from pandas import errors
+
+            with tm.assert_produces_warning(errors.PandasFutureWarning):
+                # run the same checks as below while asserting we warned
+                result = getattr(df, method)(numeric_only=numeric_only)
+                expected = Series([unit, unit, unit], index=idx, dtype="float64")
+                tm.assert_series_equal(result, expected)
+
+                result = getattr(df, method)(numeric_only=numeric_only, min_count=1)
+                expected = Series([unit, unit, np.nan], index=idx)
+                tm.assert_series_equal(result, expected)
+
+                result = getattr(df, method)(numeric_only=numeric_only, min_count=0)
+                expected = Series([unit, unit, unit], index=idx, dtype="float64")
+                tm.assert_series_equal(result, expected)
+
+                result = getattr(df.iloc[1:], method)(
+                    numeric_only=numeric_only, min_count=1
+                )
+                expected = Series([unit, np.nan, np.nan], index=idx)
+                tm.assert_series_equal(result, expected)
+
+                # min_count > 1 cases
+                df2 = DataFrame({"A": [unit] * 10, "B": [unit] * 5 + [np.nan] * 5})
+                result = getattr(df2, method)(numeric_only=numeric_only, min_count=5)
+                expected = Series(result, index=["A", "B"])
+                tm.assert_series_equal(result, expected)
+
+                result = getattr(df2, method)(numeric_only=numeric_only, min_count=6)
+                expected = Series(result, index=["A", "B"])
+                tm.assert_series_equal(result, expected)
+            return
         # The default
         result = getattr(df, method)(numeric_only=numeric_only)
         expected = Series([unit, unit, unit], index=idx, dtype="float64")
@@ -1757,8 +1792,14 @@ def test_any_all_categorical_dtype_nuisance_column(self, all_boolean_reductions)
         with pytest.raises(TypeError, match="does not support operation"):
             getattr(df, all_boolean_reductions)(bool_only=False)
 
-        with pytest.raises(TypeError, match="does not support operation"):
-            getattr(df, all_boolean_reductions)(bool_only=None)
+        # With the deprecation in place, passing None should emit a
+        # PandasFutureWarning and then the operation should raise the
+        # original TypeError. Capture both.
+        from pandas import errors
+
+        with tm.assert_produces_warning(errors.PandasFutureWarning):
+            with pytest.raises(TypeError, match="does not support operation"):
+                getattr(df, all_boolean_reductions)(bool_only=None)
 
         with pytest.raises(TypeError, match="does not support operation"):
             getattr(np, all_boolean_reductions)(df, axis=0)
@@ -1995,6 +2036,20 @@ def test_minmax_extensionarray(method, numeric_only):
     int64_info = np.iinfo("int64")
     ser = Series([int64_info.max, None, int64_info.min], dtype=pd.Int64Dtype())
     df = DataFrame({"Int64": ser})
+    # New behavior: numeric_only=None is deprecated; emit a warning but
+    # continue to accept it during the deprecation period.
+    if numeric_only is None:
+        from pandas import errors
+
+        with tm.assert_produces_warning(errors.PandasFutureWarning):
+            result = getattr(df, method)(numeric_only=numeric_only)
+            expected = Series(
+                [getattr(int64_info, method)],
+                dtype="Int64",
+                index=Index(["Int64"]),
+            )
+            tm.assert_series_equal(result, expected)
+        return
     result = getattr(df, method)(numeric_only=numeric_only)
     expected = Series(
         [getattr(int64_info, method)],

pandas/util/_validators.py

@@ -269,6 +269,9 @@ def validate_bool_kwarg(
     return value
 
 
+# deprecate_numeric_only_none defined later in file
+
+
 def validate_fillna_kwargs(value, method, validate_scalar_dict_value: bool = True):
     """
     Validate the keyword arguments to 'fillna'.
@@ -341,6 +344,31 @@ def validate_percentile(q: float | Iterable[float]) -> np.ndarray:
     return q_arr
 
 
+def deprecate_numeric_only_none(value: BoolishNoneT, arg_name: str) -> BoolishNoneT:
+    """
+    Deprecation helper for the "numeric_only" argument when value is None.
+
+    If ``value`` is ``None``, emit a PandasFutureWarning indicating that
+    passing ``None`` for ``numeric_only`` is deprecated and will be an error
+    in a future version. Return the input value unchanged.
+
+    This helper allows a warn-first / error-later migration strategy during
+    the 2.x release cycle: callers can call this to warn users for now, and
+    later releases should enforce strict bool-only semantics.
+    """
+    import warnings
+
+    from pandas import errors
+
+    if value is None:
+        msg = (
+            f'Passing None for "{arg_name}" is deprecated and will raise a '
+            "ValueError in a future version; please pass True or False."
+        )
+        warnings.warn(msg, errors.PandasFutureWarning, stacklevel=2)
+    return value
+
+
 @overload
 def validate_ascending(ascending: BoolishT) -> BoolishT: ...