CLN: enforce deprecation of the kind keyword on df.resample/ser.resample

doc/source/whatsnew/v3.0.0.rst

@@ -215,6 +215,7 @@ Removal of prior version deprecations/changes
 - Disallow units other than "s", "ms", "us", "ns" for datetime64 and timedelta64 dtypes in :func:`array` (:issue:`53817`)
 - Removed "freq" keyword from :class:`PeriodArray` constructor, use "dtype" instead (:issue:`52462`)
 - Removed 'fastpath' keyword in :class:`Categorical` constructor (:issue:`20110`)
+- Removed 'kind' keyword in :meth:`Series.resample` and :meth:`DataFrame.resample` (:issue:`58125`)
 - Removed alias :class:`arrays.PandasArray` for :class:`arrays.NumpyExtensionArray` (:issue:`53694`)
 - Removed deprecated "method" and "limit" keywords from :meth:`Series.replace` and :meth:`DataFrame.replace` (:issue:`53492`)
 - Removed extension test classes ``BaseNoReduceTests``, ``BaseNumericReduceTests``, ``BaseBooleanReduceTests`` (:issue:`54663`)

pandas/core/generic.py

@@ -8615,7 +8615,6 @@ def resample(
         closed: Literal["right", "left"] | None = None,
         label: Literal["right", "left"] | None = None,
         convention: Literal["start", "end", "s", "e"] | lib.NoDefault = lib.no_default,
-        kind: Literal["timestamp", "period"] | None | lib.NoDefault = lib.no_default,
         on: Level | None = None,
         level: Level | None = None,
         origin: str | TimestampConvertibleTypes = "start_day",
@@ -8648,14 +8647,6 @@ def resample(
 
             .. deprecated:: 2.2.0
                 Convert PeriodIndex to DatetimeIndex before resampling instead.
-        kind : {{'timestamp', 'period'}}, optional, default None
-            Pass 'timestamp' to convert the resulting index to a
-            `DateTimeIndex` or 'period' to convert it to a `PeriodIndex`.
-            By default the input representation is retained.
-
-            .. deprecated:: 2.2.0
-                Convert index to desired type explicitly instead.
-
         on : str, optional
             For a DataFrame, column to use instead of index for resampling.
             Column must be datetime-like.
@@ -8947,18 +8938,6 @@ def resample(
         """
         from pandas.core.resample import get_resampler
 
-        if kind is not lib.no_default:
-            # GH#55895
-            warnings.warn(
-                f"The 'kind' keyword in {type(self).__name__}.resample is "
-                "deprecated and will be removed in a future version. "
-                "Explicitly cast the index to the desired type instead",
-                FutureWarning,
-                stacklevel=find_stack_level(),
-            )
-        else:
-            kind = None
-
         if convention is not lib.no_default:
             warnings.warn(
                 f"The 'convention' keyword in {type(self).__name__}.resample is "
@@ -8976,7 +8955,6 @@ def resample(
             freq=rule,
             label=label,
             closed=closed,
-            kind=kind,
             convention=convention,
             key=on,
             level=level,

pandas/core/resample.py

@@ -130,8 +130,6 @@ class Resampler(BaseGroupBy, PandasObject):
     ----------
     obj : Series or DataFrame
     groupby : TimeGrouper
-    kind : str or None
-        'period', 'timestamp' to override default index treatment
 
     Returns
     -------
@@ -154,7 +152,6 @@ class Resampler(BaseGroupBy, PandasObject):
         "closed",
         "label",
         "convention",
-        "kind",
         "origin",
         "offset",
     ]
@@ -163,7 +160,6 @@ def __init__(
         self,
         obj: NDFrame,
         timegrouper: TimeGrouper,
-        kind=None,
         *,
         gpr_index: Index,
         group_keys: bool = False,
@@ -173,7 +169,6 @@ def __init__(
         self._timegrouper = timegrouper
         self.keys = None
         self.sort = True
-        self.kind = kind
         self.group_keys = group_keys
         self.as_index = True
         self.include_groups = include_groups
@@ -1580,7 +1575,7 @@ def _resampler_for_grouping(self) -> type[DatetimeIndexResamplerGroupby]:
 
     def _get_binner_for_time(self):
         # this is how we are actually creating the bins
-        if self.kind == "period":
+        if isinstance(self.ax, PeriodIndex):
             return self._timegrouper._get_time_period_bins(self.ax)
         return self._timegrouper._get_time_bins(self.ax)
 
@@ -1678,7 +1673,9 @@ def _wrap_result(self, result):
 
         # we may have a different kind that we were asked originally
         # convert if needed
-        if self.kind == "period" and not isinstance(result.index, PeriodIndex):
+        if isinstance(self.ax, PeriodIndex) and not isinstance(
+            result.index, PeriodIndex
+        ):
             if isinstance(result.index, MultiIndex):
                 # GH 24103 - e.g. groupby resample
                 if not isinstance(result.index.levels[-1], PeriodIndex):
@@ -1719,7 +1716,7 @@ def _resampler_for_grouping(self):
         return PeriodIndexResamplerGroupby
 
     def _get_binner_for_time(self):
-        if self.kind == "timestamp":
+        if isinstance(self.ax, DatetimeIndex):
             return super()._get_binner_for_time()
         return self._timegrouper._get_period_bins(self.ax)
 
@@ -1736,7 +1733,7 @@ def _convert_obj(self, obj: NDFrameT) -> NDFrameT:
             raise NotImplementedError(msg)
 
         # convert to timestamp
-        if self.kind == "timestamp":
+        if isinstance(obj, DatetimeIndex):
             obj = obj.to_timestamp(how=self.convention)
 
         return obj
@@ -1751,7 +1748,7 @@ def _downsample(self, how, **kwargs):
         **kwargs : kw args passed to how function
         """
         # we may need to actually resample as if we are timestamps
-        if self.kind == "timestamp":
+        if isinstance(self.ax, DatetimeIndex):
             return super()._downsample(how, **kwargs)
 
         ax = self.ax
@@ -1788,7 +1785,7 @@ def _upsample(self, method, limit: int | None = None, fill_value=None):
             Value to use for missing values.
         """
         # we may need to actually resample as if we are timestamps
-        if self.kind == "timestamp":
+        if isinstance(self.ax, DatetimeIndex):
             return super()._upsample(method, limit=limit, fill_value=fill_value)
 
         ax = self.ax
@@ -1860,12 +1857,12 @@ def _resampler_cls(self):
         return TimedeltaIndexResampler
 
 
-def get_resampler(obj: Series | DataFrame, kind=None, **kwds) -> Resampler:
+def get_resampler(obj: Series | DataFrame, **kwds) -> Resampler:
     """
     Create a TimeGrouper and return our resampler.
     """
     tg = TimeGrouper(obj, **kwds)  # type: ignore[arg-type]
-    return tg._get_resampler(obj, kind=kind)
+    return tg._get_resampler(obj)
 
 
 get_resampler.__doc__ = Resampler.__doc__
@@ -1877,7 +1874,6 @@ def get_resampler_for_grouping(
     how=None,
     fill_method=None,
     limit: int | None = None,
-    kind=None,
     on=None,
     include_groups: bool = True,
     **kwargs,
@@ -1887,7 +1883,7 @@ def get_resampler_for_grouping(
     """
     # .resample uses 'on' similar to how .groupby uses 'key'
     tg = TimeGrouper(freq=rule, key=on, **kwargs)
-    resampler = tg._get_resampler(groupby.obj, kind=kind)
+    resampler = tg._get_resampler(groupby.obj)
     return resampler._get_resampler_for_grouping(
         groupby=groupby, include_groups=include_groups, key=tg.key
     )
@@ -1910,7 +1906,6 @@ class TimeGrouper(Grouper):
         "closed",
         "label",
         "how",
-        "kind",
         "convention",
         "origin",
         "offset",
@@ -1928,7 +1923,6 @@ def __init__(
         how: str = "mean",
         fill_method=None,
         limit: int | None = None,
-        kind: str | None = None,
         convention: Literal["start", "end", "e", "s"] | None = None,
         origin: Literal["epoch", "start", "start_day", "end", "end_day"]
         | TimestampConvertibleTypes = "start_day",
@@ -1986,7 +1980,6 @@ def __init__(
 
         self.closed = closed
         self.label = label
-        self.kind = kind
         self.convention = convention if convention is not None else "e"
         self.how = how
         self.fill_method = fill_method
@@ -2024,15 +2017,13 @@ def __init__(
 
         super().__init__(freq=freq, key=key, **kwargs)
 
-    def _get_resampler(self, obj: NDFrame, kind=None) -> Resampler:
+    def _get_resampler(self, obj: NDFrame) -> Resampler:
         """
         Return my resampler or raise if we have an invalid axis.
 
         Parameters
         ----------
         obj : Series or DataFrame
-        kind : string, optional
-            'period','timestamp','timedelta' are valid
 
         Returns
         -------
@@ -2048,11 +2039,10 @@ def _get_resampler(self, obj: NDFrame, kind=None) -> Resampler:
             return DatetimeIndexResampler(
                 obj,
                 timegrouper=self,
-                kind=kind,
                 group_keys=self.group_keys,
                 gpr_index=ax,
             )
-        elif isinstance(ax, PeriodIndex) or kind == "period":
+        elif isinstance(ax, PeriodIndex):
             if isinstance(ax, PeriodIndex):
                 # GH#53481
                 warnings.warn(
@@ -2061,17 +2051,9 @@ def _get_resampler(self, obj: NDFrame, kind=None) -> Resampler:
                     FutureWarning,
                     stacklevel=find_stack_level(),
                 )
-            else:
-                warnings.warn(
-                    "Resampling with kind='period' is deprecated.  "
-                    "Use datetime paths instead.",
-                    FutureWarning,
-                    stacklevel=find_stack_level(),
-                )
             return PeriodIndexResampler(
                 obj,
                 timegrouper=self,
-                kind=kind,
                 group_keys=self.group_keys,
                 gpr_index=ax,
             )

pandas/tests/resample/test_datetime_index.py

@@ -441,19 +441,6 @@ def test_resample_frame_basic_M_A(freq, unit):
     tm.assert_series_equal(result["A"], df["A"].resample(freq).mean())
 
 
-@pytest.mark.parametrize("freq", ["W-WED", "ME"])
-def test_resample_frame_basic_kind(freq, unit):
-    df = DataFrame(
-        np.random.default_rng(2).standard_normal((10, 4)),
-        columns=Index(list("ABCD"), dtype=object),
-        index=date_range("2000-01-01", periods=10, freq="B"),
-    )
-    df.index = df.index.as_unit(unit)
-    msg = "The 'kind' keyword in DataFrame.resample is deprecated"
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        df.resample(freq, kind="period").mean()
-
-
 def test_resample_upsample(unit):
     # from daily
     dti = date_range(
@@ -665,9 +652,7 @@ def test_resample_timestamp_to_period(
     ts = simple_date_range_series("1/1/1990", "1/1/2000")
     ts.index = ts.index.as_unit(unit)
 
-    msg = "The 'kind' keyword in Series.resample is deprecated"
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        result = ts.resample(freq, kind="period").mean()
+    result = ts.resample(freq).mean().to_period()
     expected = ts.resample(freq).mean()
     expected.index = period_range(**expected_kwargs)
     tm.assert_series_equal(result, expected)
@@ -985,9 +970,7 @@ def test_resample_to_period_monthly_buglet(unit):
     rng = date_range("1/1/2000", "12/31/2000").as_unit(unit)
     ts = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng)
 
-    msg = "The 'kind' keyword in Series.resample is deprecated"
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        result = ts.resample("ME", kind="period").mean()
+    result = ts.resample("ME").mean().to_period()
     exp_index = period_range("Jan-2000", "Dec-2000", freq="M")
     tm.assert_index_equal(result.index, exp_index)
 
@@ -1109,18 +1092,15 @@ def test_resample_anchored_intraday(unit):
     df = DataFrame(rng.month, index=rng)
 
     result = df.resample("ME").mean()
-    msg = "The 'kind' keyword in DataFrame.resample is deprecated"
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        expected = df.resample("ME", kind="period").mean().to_timestamp(how="end")
+    expected = df.resample("ME").mean().to_period()
+    expected = expected.to_timestamp(how="end")
     expected.index += Timedelta(1, "ns") - Timedelta(1, "D")
     expected.index = expected.index.as_unit(unit)._with_freq("infer")
     assert expected.index.freq == "ME"
     tm.assert_frame_equal(result, expected)
 
     result = df.resample("ME", closed="left").mean()
-    msg = "The 'kind' keyword in DataFrame.resample is deprecated"
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        exp = df.shift(1, freq="D").resample("ME", kind="period").mean()
+    exp = df.shift(1, freq="D").resample("ME").mean().to_period()
     exp = exp.to_timestamp(how="end")
 
     exp.index = exp.index + Timedelta(1, "ns") - Timedelta(1, "D")
@@ -1134,21 +1114,17 @@ def test_resample_anchored_intraday2(unit):
     df = DataFrame(rng.month, index=rng)
 
     result = df.resample("QE").mean()
-    msg = "The 'kind' keyword in DataFrame.resample is deprecated"
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        expected = df.resample("QE", kind="period").mean().to_timestamp(how="end")
+    expected = df.resample("QE").mean().to_period()
+    expected = expected.to_timestamp(how="end")
     expected.index += Timedelta(1, "ns") - Timedelta(1, "D")
     expected.index._data.freq = "QE"
     expected.index._freq = lib.no_default
     expected.index = expected.index.as_unit(unit)
     tm.assert_frame_equal(result, expected)
 
     result = df.resample("QE", closed="left").mean()
-    msg = "The 'kind' keyword in DataFrame.resample is deprecated"
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        expected = (
-            df.shift(1, freq="D").resample("QE", kind="period", closed="left").mean()
-        )
+    expected = df.shift(1, freq="D").resample("QE").mean()
+    expected = expected.to_period()
     expected = expected.to_timestamp(how="end")
     expected.index += Timedelta(1, "ns") - Timedelta(1, "D")
     expected.index._data.freq = "QE"
@@ -1205,9 +1181,7 @@ def test_corner_cases_date(simple_date_range_series, unit):
     # resample to periods
     ts = simple_date_range_series("2000-04-28", "2000-04-30 11:00", freq="h")
     ts.index = ts.index.as_unit(unit)
-    msg = "The 'kind' keyword in Series.resample is deprecated"
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        result = ts.resample("ME", kind="period").mean()
+    result = ts.resample("ME").mean().to_period()
     assert len(result) == 1
     assert result.index[0] == Period("2000-04", freq="M")