DEPR update dedeprecation message for np.ptp #28665 #6581

pandas/core/generic.py

@@ -10184,39 +10184,42 @@ def mad(self, axis=None, skipna=None, level=None):
             _min_examples,
         )
 
-    @classmethod
-    def _add_series_only_operations(cls):
-        """
-        Add the series only operations to the cls; evaluate the doc
-        strings again.
-        """
-
-        axis_descr, name, name2 = _doc_parms(cls)
-
-        def nanptp(values, axis=0, skipna=True):
-            nmax = nanops.nanmax(values, axis, skipna)
-            nmin = nanops.nanmin(values, axis, skipna)
-            warnings.warn(
-                "Method .ptp is deprecated and will be removed "
-                "in a future version. Use numpy.ptp instead.",
-                FutureWarning,
-                stacklevel=4,
-            )
-            return nmax - nmin
-
-        cls.ptp = _make_stat_function(
-            cls,
-            "ptp",
-            name,
-            name2,
-            axis_descr,
-            """Return the difference between the min and max value.
-            \n.. deprecated:: 0.24.0 Use numpy.ptp instead
-            \nReturn the difference between the maximum value and the
-            minimum value in the object. This is the equivalent of the
-            ``numpy.ndarray`` method ``ptp``.""",
-            nanptp,
-        )
+    # @classmethod
+    # def _add_series_only_operations(cls):
+    #     """
+    #     Add the series only operations to the cls; evaluate the doc
+    #     strings again.
+    #     """
+
+    #     axis_descr, name, name2 = _doc_parms(cls)
+
+    #     def nanptp(values, axis=0, skipna=True):
+    #         nmax = nanops.nanmax(values, axis, skipna)
+    #         nmin = nanops.nanmin(values, axis, skipna)
+    #         warnings.warn(
+    #             "Method .ptp is deprecated and will be removed "
+    #             "in a future version. Use numpy.ptp instead."
+    #             "if you are already using numpy.ptp and still getting this message,"
+    #             "please call to_numpy() to avoid this message in future calls."
+    #             "For example: np.ptp(pd.Series([1, 2, 3]).to_numpy())",
+    #             FutureWarning,
+    #             stacklevel=4,
+    #         )
+    #         return nmax - nmin
+
+    #     cls.ptp = _make_stat_function(
+    #         cls,
+    #         "ptp",
+    #         name,
+    #         name2,
+    #         axis_descr,
+    #         """Return the difference between the min and max value.
+    #         \n.. deprecated:: 0.24.0 Use numpy.ptp instead
+    #         \nReturn the difference between the maximum value and the
+    #         minimum value in the object. This is the equivalent of the
+    #         ``numpy.ndarray`` method ``ptp``.""",
+    #         nanptp,
+    #     )
 
     @classmethod
     def _add_series_or_dataframe_operations(cls):

pandas/core/series.py

@@ -4390,7 +4390,7 @@ def to_period(self, freq=None, copy=True):
     ["index"], docs={"index": "The index (axis labels) of the Series."},
 )
 Series._add_numeric_operations()
-Series._add_series_only_operations()
+# Series._add_series_only_operations()
 Series._add_series_or_dataframe_operations()
 
 # Add arithmetic!

pandas/tests/series/test_analytics.py

@@ -861,37 +861,37 @@ def test_ptp(self):
             assert np.ptp(ser) == np.ptp(arr)
 
         # GH11163
-        s = Series([3, 5, np.nan, -3, 10])
-        with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
-            assert s.ptp() == 13
-            assert pd.isna(s.ptp(skipna=False))
-
-        mi = pd.MultiIndex.from_product([["a", "b"], [1, 2, 3]])
-        s = pd.Series([1, np.nan, 7, 3, 5, np.nan], index=mi)
-
-        expected = pd.Series([6, 2], index=["a", "b"], dtype=np.float64)
-        with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
-            tm.assert_series_equal(s.ptp(level=0), expected)
-
-        expected = pd.Series([np.nan, np.nan], index=["a", "b"])
-        with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
-            tm.assert_series_equal(s.ptp(level=0, skipna=False), expected)
-
-        msg = "No axis named 1 for object type <class 'pandas.core.series.Series'>"
-        with pytest.raises(ValueError, match=msg):
-            with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
-                s.ptp(axis=1)
-
-        s = pd.Series(["a", "b", "c", "d", "e"])
-        msg = r"unsupported operand type\(s\) for -: 'str' and 'str'"
-        with pytest.raises(TypeError, match=msg):
-            with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
-                s.ptp()
-
-        msg = r"Series\.ptp does not implement numeric_only\."
-        with pytest.raises(NotImplementedError, match=msg):
-            with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
-                s.ptp(numeric_only=True)
+        # s = Series([3, 5, np.nan, -3, 10])
+        # with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+        #     assert s.ptp() == 13
+        #     assert pd.isna(s.ptp(skipna=False))
+
+        # mi = pd.MultiIndex.from_product([["a", "b"], [1, 2, 3]])
+        # s = pd.Series([1, np.nan, 7, 3, 5, np.nan], index=mi)
+
+        # expected = pd.Series([6, 2], index=["a", "b"], dtype=np.float64)
+        # with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+        #     tm.assert_series_equal(s.ptp(level=0), expected)
+
+        # expected = pd.Series([np.nan, np.nan], index=["a", "b"])
+        # with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+        #     tm.assert_series_equal(s.ptp(level=0, skipna=False), expected)
+
+        # msg = "No axis named 1 for object type <class 'pandas.core.series.Series'>"
+        # with pytest.raises(ValueError, match=msg):
+        #     with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+        #         s.ptp(axis=1)
+
+        # s = pd.Series(["a", "b", "c", "d", "e"])
+        # msg = r"unsupported operand type\(s\) for -: 'str' and 'str'"
+        # with pytest.raises(TypeError, match=msg):
+        #     with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+        #         s.ptp()
+
+        # msg = r"Series\.ptp does not implement numeric_only\."
+        # with pytest.raises(NotImplementedError, match=msg):
+        #     with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+        #         s.ptp(numeric_only=True)
 
     def test_repeat(self):
         s = Series(np.random.randn(3), index=["a", "b", "c"])