CLN: Enforce deprecation of axis=None in DataFrame reductions

asv_bench/benchmarks/stat_ops.py

@@ -33,6 +33,7 @@ def setup(self, op, axis):
                 ("median", 1),
                 ("median", None),
                 ("std", 1),
+                ("std", None),
             )
         ):
             # Skipping cases where datetime aggregations are not implemented

doc/source/whatsnew/v3.0.0.rst

@@ -197,6 +197,7 @@ Removal of prior version deprecations/changes
 - All arguments in :meth:`Series.to_dict` are now keyword only (:issue:`56493`)
 - Changed the default value of ``observed`` in :meth:`DataFrame.groupby` and :meth:`Series.groupby` to ``True`` (:issue:`51811`)
 - Enforced deprecation disallowing parsing datetimes with mixed time zones unless user passes ``utc=True`` to :func:`to_datetime` (:issue:`57275`)
+- Enforced deprecation of ``axis=None`` acting the same as ``axis=0`` in the DataFrame reductions ``sum``, ``prod``, ``std``, ``var``, and ``sem``, passing ``axis=None`` will now reduce over both axes; this is particularly the case when doing e.g. ``numpy.sum(df)`` (:issue:`21597`)
 - Enforced silent-downcasting deprecation for :ref:`all relevant methods <whatsnew_220.silent_downcasting>` (:issue:`54710`)
 - In :meth:`DataFrame.stack`, the default value of ``future_stack`` is now ``True``; specifying ``False`` will raise a ``FutureWarning`` (:issue:`55448`)
 - Methods ``apply``, ``agg``, and ``transform`` will no longer replace NumPy functions (e.g. ``np.sum``) and built-in functions (e.g. ``min``) with the equivalent pandas implementation; use string aliases (e.g. ``"sum"`` and ``"min"``) if you desire to use the pandas implementation (:issue:`53974`)
@@ -238,7 +239,6 @@ Removal of prior version deprecations/changes
 - Removed unused arguments ``*args`` and ``**kwargs`` in :class:`Resampler` methods (:issue:`50977`)
 - Unrecognized timezones when parsing strings to datetimes now raises a ``ValueError`` (:issue:`51477`)
 
-
 .. ---------------------------------------------------------------------------
 .. _whatsnew_300.performance:
 

pandas/core/frame.py

@@ -11514,7 +11514,9 @@ def sum(
             min_count=min_count,
             **kwargs,
         )
-        return result.__finalize__(self, method="sum")
+        if isinstance(result, Series):
+            result = result.__finalize__(self, method="sum")
+        return result
 
     @doc(make_doc("prod", ndim=2))
     def prod(
@@ -11532,7 +11534,9 @@ def prod(
             min_count=min_count,
             **kwargs,
         )
-        return result.__finalize__(self, method="prod")
+        if isinstance(result, Series):
+            result = result.__finalize__(self, method="prod")
+        return result
 
     # error: Signature of "mean" incompatible with supertype "NDFrame"
     @overload  # type: ignore[override]

pandas/core/generic.py

@@ -11445,7 +11445,7 @@ def _stat_function_ddof(
         self,
         name: str,
         func,
-        axis: Axis | None | lib.NoDefault = lib.no_default,
+        axis: Axis | None = 0,
         skipna: bool = True,
         ddof: int = 1,
         numeric_only: bool = False,
@@ -11454,20 +11454,6 @@ def _stat_function_ddof(
         nv.validate_stat_ddof_func((), kwargs, fname=name)
         validate_bool_kwarg(skipna, "skipna", none_allowed=False)
 
-        if axis is None:
-            if self.ndim > 1:
-                warnings.warn(
-                    f"The behavior of {type(self).__name__}.{name} with axis=None "
-                    "is deprecated, in a future version this will reduce over both "
-                    "axes and return a scalar. To retain the old behavior, pass "
-                    "axis=0 (or do not pass axis)",
-                    FutureWarning,
-                    stacklevel=find_stack_level(),
-                )
-            axis = 0
-        elif axis is lib.no_default:
-            axis = 0
-
         return self._reduce(
             func, name, axis=axis, numeric_only=numeric_only, skipna=skipna, ddof=ddof
         )
@@ -11619,7 +11605,7 @@ def _min_count_stat_function(
         self,
         name: str,
         func,
-        axis: Axis | None | lib.NoDefault = lib.no_default,
+        axis: Axis | None = 0,
         skipna: bool = True,
         numeric_only: bool = False,
         min_count: int = 0,
@@ -11630,20 +11616,6 @@ def _min_count_stat_function(
 
         validate_bool_kwarg(skipna, "skipna", none_allowed=False)
 
-        if axis is None:
-            if self.ndim > 1:
-                warnings.warn(
-                    f"The behavior of {type(self).__name__}.{name} with axis=None "
-                    "is deprecated, in a future version this will reduce over both "
-                    "axes and return a scalar. To retain the old behavior, pass "
-                    "axis=0 (or do not pass axis)",
-                    FutureWarning,
-                    stacklevel=find_stack_level(),
-                )
-            axis = 0
-        elif axis is lib.no_default:
-            axis = 0
-
         return self._reduce(
             func,
             name=name,

pandas/tests/frame/test_npfuncs.py

@@ -27,22 +27,16 @@ def test_np_sqrt(self, float_frame):
 
         tm.assert_frame_equal(result, float_frame.apply(np.sqrt))
 
-    def test_sum_deprecated_axis_behavior(self):
-        # GH#52042 deprecated behavior of df.sum(axis=None), which gets
+    def test_sum_axis_behavior(self):
+        # GH#52042 df.sum(axis=None) now reduces over both axes, which gets
         #  called when we do np.sum(df)
 
         arr = np.random.default_rng(2).standard_normal((4, 3))
         df = DataFrame(arr)
 
-        msg = "The behavior of DataFrame.sum with axis=None is deprecated"
-        with tm.assert_produces_warning(
-            FutureWarning, match=msg, check_stacklevel=False
-        ):
-            res = np.sum(df)
-
-        with tm.assert_produces_warning(FutureWarning, match=msg):
-            expected = df.sum(axis=None)
-        tm.assert_series_equal(res, expected)
+        res = np.sum(df)
+        expected = df.to_numpy().sum(axis=None)
+        assert res == expected
 
     def test_np_ravel(self):
         # GH26247

pandas/tests/groupby/aggregate/test_aggregate.py

@@ -132,9 +132,7 @@ def test_agg_apply_corner(ts, tsframe):
     tm.assert_frame_equal(grouped.sum(), exp_df)
     tm.assert_frame_equal(grouped.agg("sum"), exp_df)
 
-    msg = "The behavior of DataFrame.sum with axis=None is deprecated"
-    with tm.assert_produces_warning(FutureWarning, match=msg, check_stacklevel=False):
-        res = grouped.apply(np.sum)
+    res = grouped.apply(np.sum, axis=0)
     tm.assert_frame_equal(res, exp_df)
 
 

pandas/tests/groupby/test_raises.py

@@ -222,11 +222,7 @@ def test_groupby_raises_string_np(
             "Could not convert string .* to numeric",
         ),
     }[groupby_func_np]
-    if how == "transform" and groupby_func_np is np.sum and not groupby_series:
-        warn_msg = "The behavior of DataFrame.sum with axis=None is deprecated"
-    else:
-        warn_msg = ""
-    _call_and_check(klass, msg, how, gb, groupby_func_np, (), warn_msg)
+    _call_and_check(klass, msg, how, gb, groupby_func_np, ())
 
 
 @pytest.mark.parametrize("how", ["method", "agg", "transform"])

pandas/tests/window/test_expanding.py

@@ -310,7 +310,7 @@ def test_expanding_corr_pairwise(frame):
 @pytest.mark.parametrize(
     "func,static_comp",
     [
-        ("sum", np.sum),
+        ("sum", lambda x: np.sum(x, axis=0)),
         ("mean", lambda x: np.mean(x, axis=0)),
         ("max", lambda x: np.max(x, axis=0)),
         ("min", lambda x: np.min(x, axis=0)),
@@ -324,12 +324,7 @@ def test_expanding_func(func, static_comp, frame_or_series):
     result = getattr(obj, func)()
     assert isinstance(result, frame_or_series)
 
-    msg = "The behavior of DataFrame.sum with axis=None is deprecated"
-    warn = None
-    if frame_or_series is DataFrame and static_comp is np.sum:
-        warn = FutureWarning
-    with tm.assert_produces_warning(warn, match=msg, check_stacklevel=False):
-        expected = static_comp(data[:11])
+    expected = static_comp(data[:11])
     if frame_or_series is Series:
         tm.assert_almost_equal(result[10], expected)
     else: