DEPR: Enforce ewm(times=str) deprecation (pandas-dev#48842)

* DEPR: Enforce ewm(times=str) deprecation

* Fix typing

* Fix test

* convert test to raises

* Add whatsnew

* Fix typing

* fix typing

Author: mroeschke

doc/source/whatsnew/v2.0.0.rst

@@ -178,6 +178,7 @@ Removal of prior version deprecations/changes
 - Remove :meth:`DataFrameGroupBy.pad` and :meth:`DataFrameGroupBy.backfill` (:issue:`45076`)
 - Remove ``numpy`` argument from :func:`read_json` (:issue:`30636`)
 - Removed the ``center`` keyword in :meth:`DataFrame.expanding` (:issue:`20647`)
+- Enforced disallowing a string column label into ``times`` in :meth:`DataFrame.ewm` (:issue:`43265`)
 - Enforced :meth:`Rolling.count` with ``min_periods=None`` to default to the size of the window (:issue:`31302`)
 -
 

pandas/core/generic.py

@@ -12089,7 +12089,7 @@ def ewm(
         adjust: bool_t = True,
         ignore_na: bool_t = False,
         axis: Axis = 0,
-        times: str | np.ndarray | DataFrame | Series | None = None,
+        times: np.ndarray | DataFrame | Series | None = None,
         method: str = "single",
     ) -> ExponentialMovingWindow:
         axis = self._get_axis_number(axis)

pandas/core/window/ewm.py

@@ -3,10 +3,7 @@
 import datetime
 from functools import partial
 from textwrap import dedent
-from typing import (
-    TYPE_CHECKING,
-    cast,
-)
+from typing import TYPE_CHECKING
 import warnings
 
 import numpy as np
@@ -106,7 +103,7 @@ def get_center_of_mass(
 
 
 def _calculate_deltas(
-    times: str | np.ndarray | NDFrame | None,
+    times: np.ndarray | NDFrame,
     halflife: float | TimedeltaConvertibleTypes | None,
 ) -> np.ndarray:
     """
@@ -115,7 +112,7 @@ def _calculate_deltas(
 
     Parameters
     ----------
-    times : str, np.ndarray, Series, default None
+    times : np.ndarray, Series
         Times corresponding to the observations. Must be monotonically increasing
         and ``datetime64[ns]`` dtype.
     halflife : float, str, timedelta, optional
@@ -126,13 +123,7 @@ def _calculate_deltas(
     np.ndarray
         Diff of the times divided by the half-life
     """
-    # error: Item "str" of "Union[str, ndarray, NDFrameT, None]" has no
-    # attribute "view"
-    # error: Item "None" of "Union[str, ndarray, NDFrameT, None]" has no
-    # attribute "view"
-    _times = np.asarray(
-        times.view(np.int64), dtype=np.float64  # type: ignore[union-attr]
-    )
+    _times = np.asarray(times.view(np.int64), dtype=np.float64)
     # TODO: generalize to non-nano?
     _halflife = float(Timedelta(halflife)._as_unit("ns").value)
     return np.diff(_times) / _halflife
@@ -221,7 +212,7 @@ class ExponentialMovingWindow(BaseWindow):
 
         For `Series` this parameter is unused and defaults to 0.
 
-    times : str, np.ndarray, Series, default None
+    times : np.ndarray, Series, default None
 
         .. versionadded:: 1.1.0
 
@@ -232,9 +223,6 @@ class ExponentialMovingWindow(BaseWindow):
 
         If 1-D array like, a sequence with the same shape as the observations.
 
-        .. deprecated:: 1.4.0
-            If str, the name of the column in the DataFrame representing the times.
-
     method : str {'single', 'table'}, default 'single'
         .. versionadded:: 1.4.0
 
@@ -359,7 +347,7 @@ def __init__(
         adjust: bool = True,
         ignore_na: bool = False,
         axis: Axis = 0,
-        times: str | np.ndarray | NDFrame | None = None,
+        times: np.ndarray | NDFrame | None = None,
         method: str = "single",
         *,
         selection=None,
@@ -384,18 +372,6 @@ def __init__(
         if self.times is not None:
             if not self.adjust:
                 raise NotImplementedError("times is not supported with adjust=False.")
-            if isinstance(self.times, str):
-                warnings.warn(
-                    (
-                        "Specifying times as a string column label is deprecated "
-                        "and will be removed in a future version. Pass the column "
-                        "into times instead."
-                    ),
-                    FutureWarning,
-                    stacklevel=find_stack_level(),
-                )
-                # self.times cannot be str anymore
-                self.times = cast("Series", self._selected_obj[self.times])
             if not is_datetime64_ns_dtype(self.times):
                 raise ValueError("times must be datetime64[ns] dtype.")
             if len(self.times) != len(obj):
@@ -897,7 +873,7 @@ def __init__(self, obj, *args, _grouper=None, **kwargs) -> None:
             # sort the times and recalculate the deltas according to the groups
             groupby_order = np.concatenate(list(self._grouper.indices.values()))
             self._deltas = _calculate_deltas(
-                self.times.take(groupby_order),  # type: ignore[union-attr]
+                self.times.take(groupby_order),
                 self.halflife,
             )
 
@@ -928,7 +904,7 @@ def __init__(
         adjust: bool = True,
         ignore_na: bool = False,
         axis: Axis = 0,
-        times: str | np.ndarray | NDFrame | None = None,
+        times: np.ndarray | NDFrame | None = None,
         engine: str = "numba",
         engine_kwargs: dict[str, bool] | None = None,
         *,

pandas/tests/window/test_ewm.py

@@ -236,15 +236,13 @@ def test_float_dtype_ewma(func, expected, float_numpy_dtype):
     tm.assert_frame_equal(result, expected)
 
 
-def test_times_string_col_deprecated():
+def test_times_string_col_raises():
     # GH 43265
-    data = np.arange(10.0)
-    data[::2] = np.nan
-    df = DataFrame({"A": data, "time_col": date_range("2000", freq="D", periods=10)})
-    with tm.assert_produces_warning(FutureWarning, match="Specifying times"):
-        result = df.ewm(halflife="1 day", min_periods=0, times="time_col").mean()
-        expected = df.ewm(halflife=1.0, min_periods=0).mean()
-    tm.assert_frame_equal(result, expected)
+    df = DataFrame(
+        {"A": np.arange(10.0), "time_col": date_range("2000", freq="D", periods=10)}
+    )
+    with pytest.raises(ValueError, match="times must be datetime64"):
+        df.ewm(halflife="1 day", min_periods=0, times="time_col")
 
 
 def test_ewm_sum_adjust_false_notimplemented():

pandas/tests/window/test_groupby.py

@@ -1162,7 +1162,11 @@ def test_times(self, times_frame):
         halflife = "23 days"
         with tm.assert_produces_warning(FutureWarning, match="nuisance"):
             # GH#42738
-            result = times_frame.groupby("A").ewm(halflife=halflife, times="C").mean()
+            result = (
+                times_frame.groupby("A")
+                .ewm(halflife=halflife, times=times_frame["C"])
+                .mean()
+            )
         expected = DataFrame(
             {
                 "B": [
@@ -1201,9 +1205,13 @@ def test_times_vs_apply(self, times_frame):
         halflife = "23 days"
         with tm.assert_produces_warning(FutureWarning, match="nuisance"):
             # GH#42738
-            result = times_frame.groupby("A").ewm(halflife=halflife, times="C").mean()
+            result = (
+                times_frame.groupby("A")
+                .ewm(halflife=halflife, times=times_frame["C"])
+                .mean()
+            )
             expected = times_frame.groupby("A", group_keys=True).apply(
-                lambda x: x.ewm(halflife=halflife, times="C").mean()
+                lambda x: x.ewm(halflife=halflife, times=x["C"]).mean()
             )
         tm.assert_frame_equal(result, expected)
 
@@ -1213,7 +1221,7 @@ def test_times_array(self, times_frame):
         gb = times_frame.groupby("A")
         with tm.assert_produces_warning(FutureWarning, match="nuisance"):
             # GH#42738
-            result = gb.ewm(halflife=halflife, times="C").mean()
+            result = gb.ewm(halflife=halflife, times=times_frame["C"]).mean()
             expected = gb.ewm(halflife=halflife, times=times_frame["C"].values).mean()
         tm.assert_frame_equal(result, expected)