feat!: Removes inplace option for `pandas.core.resample.Resampler.i…

doc/source/whatsnew/v3.0.0.rst

@@ -224,6 +224,7 @@ Other API changes
 - 3rd party ``py.path`` objects are no longer explicitly supported in IO methods. Use :py:class:`pathlib.Path` objects instead (:issue:`57091`)
 - :func:`read_table`'s ``parse_dates`` argument defaults to ``None`` to improve consistency with :func:`read_csv` (:issue:`57476`)
 - Made ``dtype`` a required argument in :meth:`ExtensionArray._from_sequence_of_strings` (:issue:`56519`)
+- Removed ``inplace`` argument in :meth:`Resampler.interpolate`. (:issue:`58690`)
 - Updated :meth:`DataFrame.to_excel` so that the output spreadsheet has no styling. Custom styling can still be done using :meth:`Styler.to_excel` (:issue:`54154`)
 - pickle and HDF (``.h5``) files created with Python 2 are no longer explicitly supported (:issue:`57387`)
 - pickled objects from pandas version less than ``1.0.0`` are no longer supported (:issue:`57155`)

pandas/core/resample.py

@@ -762,7 +762,6 @@ def interpolate(
         *,
         axis: Axis = 0,
         limit: int | None = None,
-        inplace: bool = False,
         limit_direction: Literal["forward", "backward", "both"] = "forward",
         limit_area=None,
         downcast=lib.no_default,
@@ -808,8 +807,6 @@ def interpolate(
         limit : int, optional
             Maximum number of consecutive NaNs to fill. Must be greater than
             0.
-        inplace : bool, default False
-            Update the data in place if possible.
         limit_direction : {{'forward', 'backward', 'both'}}, Optional
             Consecutive NaNs will be filled in this direction.
 
@@ -909,6 +906,8 @@ def interpolate(
         ``07:00:00`` and ``07:00:02``.
         """
         assert downcast is lib.no_default  # just checking coverage
+        if kwargs.get("inplace"):
+            raise ValueError("The argument 'inplace' is no longer supported.")
         result = self._upsample("asfreq")
 
         # If the original data has timestamps which are not aligned with the
@@ -942,7 +941,7 @@ def interpolate(
             method=method,
             axis=axis,
             limit=limit,
-            inplace=inplace,
+            inplace=False,
             limit_direction=limit_direction,
             limit_area=limit_area,
             downcast=downcast,

pandas/tests/copy_view/test_interp_fillna.py

@@ -146,7 +146,7 @@ def test_interpolate_downcast_reference_triggers_copy():
 
     msg = "Can not interpolate with method=pad"
     with pytest.raises(ValueError, match=msg):
-        df.interpolate(method="pad", inplace=True, downcast="infer")
+        df.interpolate(method="pad", inplace=False, downcast="infer")
         assert df._mgr._has_no_reference(0)
         assert not np.shares_memory(arr_a, get_array(df, "a"))
 
@@ -285,7 +285,7 @@ def test_fillna_chained_assignment():
     tm.assert_frame_equal(df, df_orig)
 
 
-@pytest.mark.parametrize("func", ["interpolate", "ffill", "bfill"])
+@pytest.mark.parametrize("func", ["ffill", "bfill"])
 def test_interpolate_chained_assignment(func):
     df = DataFrame({"a": [1, np.nan, 2], "b": 1})
     df_orig = df.copy()

pandas/tests/frame/methods/test_interpolate.py

@@ -51,19 +51,6 @@ def test_interpolate_datetimelike_values(self, frame_or_series):
         expected_td = frame_or_series(orig - orig[0])
         tm.assert_equal(res_td, expected_td)
 
-    def test_interpolate_inplace(self, frame_or_series, request):
-        # GH#44749
-        obj = frame_or_series([1, np.nan, 2])
-        orig = obj.values
-
-        obj.interpolate(inplace=True)
-        expected = frame_or_series([1, 1.5, 2])
-        tm.assert_equal(obj, expected)
-
-        # check we operated *actually* inplace
-        assert np.shares_memory(orig, obj.values)
-        assert orig.squeeze()[1] == 1.5
-
     @pytest.mark.xfail(
         using_pyarrow_string_dtype(), reason="interpolate doesn't work for string"
     )
@@ -80,15 +67,6 @@ def test_interp_basic(self):
         with pytest.raises(TypeError, match=msg):
             df.interpolate()
 
-        cvalues = df["C"]._values
-        dvalues = df["D"].values
-        with pytest.raises(TypeError, match=msg):
-            df.interpolate(inplace=True)
-
-        # check we DID operate inplace
-        assert np.shares_memory(df["C"]._values, cvalues)
-        assert np.shares_memory(df["D"]._values, dvalues)
-
     @pytest.mark.xfail(
         using_pyarrow_string_dtype(), reason="interpolate doesn't work for string"
     )
@@ -307,26 +285,6 @@ def test_interp_raise_on_all_object_dtype(self):
         with pytest.raises(TypeError, match=msg):
             df.interpolate()
 
-    def test_interp_inplace(self):
-        df = DataFrame({"a": [1.0, 2.0, np.nan, 4.0]})
-        expected = df.copy()
-        result = df.copy()
-
-        with tm.raises_chained_assignment_error():
-            return_value = result["a"].interpolate(inplace=True)
-        assert return_value is None
-        tm.assert_frame_equal(result, expected)
-
-    def test_interp_inplace_row(self):
-        # GH 10395
-        result = DataFrame(
-            {"a": [1.0, 2.0, 3.0, 4.0], "b": [np.nan, 2.0, 3.0, 4.0], "c": [3, 2, 2, 2]}
-        )
-        expected = result.interpolate(method="linear", axis=1, inplace=False)
-        return_value = result.interpolate(method="linear", axis=1, inplace=True)
-        assert return_value is None
-        tm.assert_frame_equal(result, expected)
-
     def test_interp_ignore_all_good(self):
         # GH
         df = DataFrame(
@@ -352,19 +310,6 @@ def test_interp_ignore_all_good(self):
         result = df[["B", "D"]].interpolate()
         tm.assert_frame_equal(result, df[["B", "D"]])
 
-    def test_interp_time_inplace_axis(self):
-        # GH 9687
-        periods = 5
-        idx = date_range(start="2014-01-01", periods=periods)
-        data = np.random.default_rng(2).random((periods, periods))
-        data[data < 0.5] = np.nan
-        expected = DataFrame(index=idx, columns=idx, data=data)
-
-        result = expected.interpolate(axis=0, method="time")
-        return_value = expected.interpolate(axis=0, method="time", inplace=True)
-        assert return_value is None
-        tm.assert_frame_equal(result, expected)
-
     @pytest.mark.parametrize("axis_name, axis_number", [("index", 0), ("columns", 1)])
     def test_interp_string_axis(self, axis_name, axis_number):
         # https://github.com/pandas-dev/pandas/issues/25190
@@ -400,9 +345,8 @@ def test_interpolate_empty_df(self):
         # GH#53199
         df = DataFrame()
         expected = df.copy()
-        result = df.interpolate(inplace=True)
-        assert result is None
-        tm.assert_frame_equal(df, expected)
+        result = df.interpolate()
+        tm.assert_frame_equal(result, expected)
 
     def test_interpolate_ea(self, any_int_ea_dtype):
         # GH#55347

pandas/tests/generic/test_frame.py

@@ -160,9 +160,6 @@ def test_validate_bool_args(self, value):
         with pytest.raises(ValueError, match=msg):
             df.copy().replace(to_replace=1, value=7, inplace=value)
 
-        with pytest.raises(ValueError, match=msg):
-            df.copy().interpolate(inplace=value)
-
         with pytest.raises(ValueError, match=msg):
             df.copy()._where(cond=df.a > 2, inplace=value)