DEPR: idxmin/idxmax with all-NA

doc/source/whatsnew/v2.1.0.rst

@@ -309,6 +309,7 @@ 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 accepting slices in :meth:`DataFrame.take`, call ``obj[slicer]`` or pass a sequence of integers instead (:issue:`51539`)
+- Deprecated behavior of :meth:`DataFrame.idxmax`, :meth:`DataFrame.idxmin`, :meth:`Series.idxmax`, :meth:`Series.idxmin` in with all-NA entries or any-NA and ``skipna=False``; in a future version these will raise ``ValueError`` (:issue:`51276`)
 - Deprecated explicit support for subclassing :class:`Index` (:issue:`45289`)
 - Deprecated making functions given to :meth:`Series.agg` attempt to operate on each element in the :class:`Series` and only operate on the whole :class:`Series` if the elementwise operations failed. In the future, functions given to :meth:`Series.agg` will always operate on the whole :class:`Series` only. To keep the current behavior, use :meth:`Series.transform` instead. (:issue:`53325`)
 - Deprecated making the functions in a list of functions given to :meth:`DataFrame.agg` attempt to operate on each element in the :class:`DataFrame` and only operate on the columns of the :class:`DataFrame` if the elementwise operations failed. To keep the current behavior, use :meth:`DataFrame.transform` instead. (:issue:`53325`)

pandas/conftest.py

@@ -142,6 +142,10 @@ def pytest_collection_modifyitems(items, config) -> None:
         ("is_sparse", "is_sparse is deprecated"),
         ("NDFrame.replace", "The 'method' keyword"),
         ("NDFrame.replace", "Series.replace without 'value'"),
+        ("Series.idxmin", "The behavior of Series.idxmin"),
+        ("Series.idxmax", "The behavior of Series.idxmax"),
+        ("SeriesGroupBy.idxmin", "The behavior of Series.idxmin"),
+        ("SeriesGroupBy.idxmax", "The behavior of Series.idxmax"),
         # Docstring divides by zero to show behavior difference
         ("missing.mask_zero_div_zero", "divide by zero encountered"),
         (

pandas/core/frame.py

@@ -11264,6 +11264,15 @@ def idxmin(
         indices = res._values
         # indices will always be np.ndarray since axis is not N
 
+        if (indices == -1).any():
+            warnings.warn(
+                f"The behavior of {type(self).__name__}.idxmin with all-NA "
+                "values, or any-NA and skipna=False, is deprecated. In a future "
+                "version this will raise ValueError",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
         index = data._get_axis(axis)
         result = algorithms.take(
             index._values, indices, allow_fill=True, fill_value=index._na_value
@@ -11292,6 +11301,15 @@ def idxmax(
         indices = res._values
         # indices will always be 1d array since axis is not None
 
+        if (indices == -1).any():
+            warnings.warn(
+                f"The behavior of {type(self).__name__}.idxmax with all-NA "
+                "values, or any-NA and skipna=False, is deprecated. In a future "
+                "version this will raise ValueError",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
         index = data._get_axis(axis)
         result = algorithms.take(
             index._values, indices, allow_fill=True, fill_value=index._na_value

pandas/core/series.py

@@ -2536,8 +2536,16 @@ def idxmin(self, axis: Axis = 0, skipna: bool = True, *args, **kwargs) -> Hashab
             #  warning for idxmin
             warnings.simplefilter("ignore")
             i = self.argmin(axis, skipna, *args, **kwargs)
+
         if i == -1:
             # GH#43587 give correct NA value for Index.
+            warnings.warn(
+                f"The behavior of {type(self).__name__}.idxmin with all-NA "
+                "values, or any-NA and skipna=False, is deprecated. In a future "
+                "version this will raise ValueError",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
             return self.index._na_value
         return self.index[i]
 
@@ -2612,8 +2620,16 @@ def idxmax(self, axis: Axis = 0, skipna: bool = True, *args, **kwargs) -> Hashab
             #  warning for argmax
             warnings.simplefilter("ignore")
             i = self.argmax(axis, skipna, *args, **kwargs)
+
         if i == -1:
             # GH#43587 give correct NA value for Index.
+            warnings.warn(
+                f"The behavior of {type(self).__name__}.idxmax with all-NA "
+                "values, or any-NA and skipna=False, is deprecated. In a future "
+                "version this will raise ValueError",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
             return self.index._na_value
         return self.index[i]
 

pandas/tests/extension/base/methods.py

@@ -161,9 +161,12 @@ def test_argreduce_series(
     ):
         # data_missing_for_sorting -> [B, NA, A] with A < B and NA missing.
         warn = None
+        msg = "The behavior of Series.argmax/argmin"
         if op_name.startswith("arg") and expected == -1:
             warn = FutureWarning
-        msg = "The behavior of Series.argmax/argmin"
+        if op_name.startswith("idx") and np.isnan(expected):
+            warn = FutureWarning
+            msg = f"The behavior of Series.{op_name}"
         ser = pd.Series(data_missing_for_sorting)
         with tm.assert_produces_warning(warn, match=msg):
             result = getattr(ser, op_name)(skipna=skipna)

pandas/tests/frame/test_reductions.py

@@ -968,8 +968,16 @@ def test_idxmin(self, float_frame, int_frame, skipna, axis):
         frame.iloc[5:10] = np.nan
         frame.iloc[15:20, -2:] = np.nan
         for df in [frame, int_frame]:
-            result = df.idxmin(axis=axis, skipna=skipna)
-            expected = df.apply(Series.idxmin, axis=axis, skipna=skipna)
+            warn = None
+            if skipna is False or axis == 1:
+                warn = None if df is int_frame else FutureWarning
+            msg = "The behavior of DataFrame.idxmin with all-NA values"
+            with tm.assert_produces_warning(warn, match=msg):
+                result = df.idxmin(axis=axis, skipna=skipna)
+
+            msg2 = "The behavior of Series.idxmin"
+            with tm.assert_produces_warning(warn, match=msg2):
+                expected = df.apply(Series.idxmin, axis=axis, skipna=skipna)
             expected = expected.astype(df.index.dtype)
             tm.assert_series_equal(result, expected)
 
@@ -1008,8 +1016,16 @@ def test_idxmax(self, float_frame, int_frame, skipna, axis):
         frame.iloc[5:10] = np.nan
         frame.iloc[15:20, -2:] = np.nan
         for df in [frame, int_frame]:
-            result = df.idxmax(axis=axis, skipna=skipna)
-            expected = df.apply(Series.idxmax, axis=axis, skipna=skipna)
+            warn = None
+            if skipna is False or axis == 1:
+                warn = None if df is int_frame else FutureWarning
+            msg = "The behavior of DataFrame.idxmax with all-NA values"
+            with tm.assert_produces_warning(warn, match=msg):
+                result = df.idxmax(axis=axis, skipna=skipna)
+
+            msg2 = "The behavior of Series.idxmax"
+            with tm.assert_produces_warning(warn, match=msg2):
+                expected = df.apply(Series.idxmax, axis=axis, skipna=skipna)
             expected = expected.astype(df.index.dtype)
             tm.assert_series_equal(result, expected)
 

pandas/tests/reductions/test_reductions.py

@@ -113,7 +113,6 @@ def test_nanargminmax(self, opname, index_or_series):
         # GH#7261
         klass = index_or_series
         arg_op = "arg" + opname if klass is Index else "idx" + opname
-        warn = FutureWarning if klass is Index else None
 
         obj = klass([NaT, datetime(2011, 11, 1)])
         assert getattr(obj, arg_op)() == 1
@@ -122,7 +121,9 @@ def test_nanargminmax(self, opname, index_or_series):
             "The behavior of (DatetimeIndex|Series).argmax/argmin with "
             "skipna=False and NAs"
         )
-        with tm.assert_produces_warning(warn, match=msg):
+        if klass is Series:
+            msg = "The behavior of Series.(idxmax|idxmin) with all-NA"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
             result = getattr(obj, arg_op)(skipna=False)
         if klass is Series:
             assert np.isnan(result)
@@ -132,7 +133,7 @@ def test_nanargminmax(self, opname, index_or_series):
         obj = klass([NaT, datetime(2011, 11, 1), NaT])
         # check DatetimeIndex non-monotonic path
         assert getattr(obj, arg_op)() == 1
-        with tm.assert_produces_warning(warn, match=msg):
+        with tm.assert_produces_warning(FutureWarning, match=msg):
             result = getattr(obj, arg_op)(skipna=False)
         if klass is Series:
             assert np.isnan(result)
@@ -836,16 +837,24 @@ def test_idxmin_dt64index(self):
         ser = Series(
             [1.0, 2.0, np.nan], index=DatetimeIndex(["NaT", "2015-02-08", "NaT"])
         )
-        res = ser.idxmin(skipna=False)
+        msg = "The behavior of Series.idxmin with all-NA values"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            res = ser.idxmin(skipna=False)
         assert res is NaT
-        res = ser.idxmax(skipna=False)
+        msg = "The behavior of Series.idxmax with all-NA values"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            res = ser.idxmax(skipna=False)
         assert res is NaT
 
         df = ser.to_frame()
-        res = df.idxmin(skipna=False)
+        msg = "The behavior of DataFrame.idxmin with all-NA values"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            res = df.idxmin(skipna=False)
         assert res.dtype == "M8[ns]"
         assert res.isna().all()
-        res = df.idxmax(skipna=False)
+        msg = "The behavior of DataFrame.idxmax with all-NA values"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            res = df.idxmax(skipna=False)
         assert res.dtype == "M8[ns]"
         assert res.isna().all()
 
@@ -859,7 +868,9 @@ def test_idxmin(self):
 
         # skipna or no
         assert string_series[string_series.idxmin()] == string_series.min()
-        assert isna(string_series.idxmin(skipna=False))
+        msg = "The behavior of Series.idxmin"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert isna(string_series.idxmin(skipna=False))
 
         # no NaNs
         nona = string_series.dropna()
@@ -868,7 +879,8 @@ def test_idxmin(self):
 
         # all NaNs
         allna = string_series * np.nan
-        assert isna(allna.idxmin())
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert isna(allna.idxmin())
 
         # datetime64[ns]
         s = Series(date_range("20130102", periods=6))
@@ -889,7 +901,9 @@ def test_idxmax(self):
 
         # skipna or no
         assert string_series[string_series.idxmax()] == string_series.max()
-        assert isna(string_series.idxmax(skipna=False))
+        msg = "The behavior of Series.idxmax with all-NA values"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert isna(string_series.idxmax(skipna=False))
 
         # no NaNs
         nona = string_series.dropna()
@@ -898,7 +912,9 @@ def test_idxmax(self):
 
         # all NaNs
         allna = string_series * np.nan
-        assert isna(allna.idxmax())
+        msg = "The behavior of Series.idxmax with all-NA values"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert isna(allna.idxmax())
 
         s = Series(date_range("20130102", periods=6))
         result = s.idxmax()
@@ -1199,10 +1215,14 @@ def test_idxminmax_with_inf(self):
         s = Series([0, -np.inf, np.inf, np.nan])
 
         assert s.idxmin() == 1
-        assert np.isnan(s.idxmin(skipna=False))
+        msg = "The behavior of Series.idxmin with all-NA values"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert np.isnan(s.idxmin(skipna=False))
 
         assert s.idxmax() == 2
-        assert np.isnan(s.idxmax(skipna=False))
+        msg = "The behavior of Series.idxmax with all-NA values"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert np.isnan(s.idxmax(skipna=False))
 
         msg = "use_inf_as_na option is deprecated"
         with tm.assert_produces_warning(FutureWarning, match=msg):