BUG: support non-nano times in ewm (#56262)

* BUG: support non-nano times in ewm

* GH ref

* update exception message

* update test

Author: jbrockmendel

doc/source/whatsnew/v2.2.0.rst

@@ -561,12 +561,14 @@ Groupby/resample/rolling
 - Bug in :meth:`.DataFrameGroupBy.idxmin`, :meth:`.DataFrameGroupBy.idxmax`, :meth:`.SeriesGroupBy.idxmin`, and :meth:`.SeriesGroupBy.idxmax` would not retain :class:`.Categorical` dtype when the index was a :class:`.CategoricalIndex` that contained NA values (:issue:`54234`)
 - Bug in :meth:`.DataFrameGroupBy.transform` and :meth:`.SeriesGroupBy.transform` when ``observed=False`` and ``f="idxmin"`` or ``f="idxmax"`` would incorrectly raise on unobserved categories (:issue:`54234`)
 - Bug in :meth:`DataFrame.asfreq` and :meth:`Series.asfreq` with a :class:`DatetimeIndex` with non-nanosecond resolution incorrectly converting to nanosecond resolution (:issue:`55958`)
+- Bug in :meth:`DataFrame.ewm` when passed ``times`` with non-nanosecond ``datetime64`` or :class:`DatetimeTZDtype` dtype (:issue:`56262`)
 - Bug in :meth:`DataFrame.resample` not respecting ``closed`` and ``label`` arguments for :class:`~pandas.tseries.offsets.BusinessDay` (:issue:`55282`)
 - Bug in :meth:`DataFrame.resample` where bin edges were not correct for :class:`~pandas.tseries.offsets.BusinessDay` (:issue:`55281`)
 - Bug in :meth:`DataFrame.resample` where bin edges were not correct for :class:`~pandas.tseries.offsets.MonthBegin` (:issue:`55271`)
 - Bug in :meth:`DataFrameGroupBy.value_counts` and :meth:`SeriesGroupBy.value_count` could result in incorrect sorting if the columns of the DataFrame or name of the Series are integers (:issue:`55951`)
 - Bug in :meth:`DataFrameGroupBy.value_counts` and :meth:`SeriesGroupBy.value_count` would not respect ``sort=False`` in :meth:`DataFrame.groupby` and :meth:`Series.groupby` (:issue:`55951`)
 - Bug in :meth:`DataFrameGroupBy.value_counts` and :meth:`SeriesGroupBy.value_count` would sort by proportions rather than frequencies when ``sort=True`` and ``normalize=True`` (:issue:`55951`)
+-
 
 Reshaping
 ^^^^^^^^^

pandas/core/window/ewm.py

@@ -12,13 +12,15 @@
 from pandas.util._decorators import doc
 
 from pandas.core.dtypes.common import (
-    is_datetime64_ns_dtype,
+    is_datetime64_dtype,
     is_numeric_dtype,
 )
+from pandas.core.dtypes.dtypes import DatetimeTZDtype
 from pandas.core.dtypes.generic import ABCSeries
 from pandas.core.dtypes.missing import isna
 
 from pandas.core import common
+from pandas.core.arrays.datetimelike import dtype_to_unit
 from pandas.core.indexers.objects import (
     BaseIndexer,
     ExponentialMovingWindowIndexer,
@@ -56,6 +58,7 @@
     from pandas._typing import (
         Axis,
         TimedeltaConvertibleTypes,
+        npt,
     )
 
     from pandas import (
@@ -101,7 +104,7 @@ def get_center_of_mass(
 def _calculate_deltas(
     times: np.ndarray | NDFrame,
     halflife: float | TimedeltaConvertibleTypes | None,
-) -> np.ndarray:
+) -> npt.NDArray[np.float64]:
     """
     Return the diff of the times divided by the half-life. These values are used in
     the calculation of the ewm mean.
@@ -119,11 +122,11 @@ def _calculate_deltas(
     np.ndarray
         Diff of the times divided by the half-life
     """
+    unit = dtype_to_unit(times.dtype)
     if isinstance(times, ABCSeries):
         times = times._values
     _times = np.asarray(times.view(np.int64), dtype=np.float64)
-    # TODO: generalize to non-nano?
-    _halflife = float(Timedelta(halflife).as_unit("ns")._value)
+    _halflife = float(Timedelta(halflife).as_unit(unit)._value)
     return np.diff(_times) / _halflife
 
 
@@ -366,8 +369,12 @@ def __init__(
         if self.times is not None:
             if not self.adjust:
                 raise NotImplementedError("times is not supported with adjust=False.")
-            if not is_datetime64_ns_dtype(self.times):
-                raise ValueError("times must be datetime64[ns] dtype.")
+            times_dtype = getattr(self.times, "dtype", None)
+            if not (
+                is_datetime64_dtype(times_dtype)
+                or isinstance(times_dtype, DatetimeTZDtype)
+            ):
+                raise ValueError("times must be datetime64 dtype.")
             if len(self.times) != len(obj):
                 raise ValueError("times must be the same length as the object.")
             if not isinstance(self.halflife, (str, datetime.timedelta, np.timedelta64)):

pandas/tests/window/test_ewm.py

@@ -60,7 +60,7 @@ def test_constructor(frame_or_series):
 
 
 def test_ewma_times_not_datetime_type():
-    msg = r"times must be datetime64\[ns\] dtype."
+    msg = r"times must be datetime64 dtype."
     with pytest.raises(ValueError, match=msg):
         Series(range(5)).ewm(times=np.arange(5))
 
@@ -102,30 +102,6 @@ def test_ewma_with_times_equal_spacing(halflife_with_times, times, min_periods):
     tm.assert_frame_equal(result, expected)
 
 
-@pytest.mark.parametrize(
-    "unit",
-    [
-        pytest.param(
-            "s",
-            marks=pytest.mark.xfail(
-                reason="ExponentialMovingWindow constructor raises on non-nano"
-            ),
-        ),
-        pytest.param(
-            "ms",
-            marks=pytest.mark.xfail(
-                reason="ExponentialMovingWindow constructor raises on non-nano"
-            ),
-        ),
-        pytest.param(
-            "us",
-            marks=pytest.mark.xfail(
-                reason="ExponentialMovingWindow constructor raises on non-nano"
-            ),
-        ),
-        "ns",
-    ],
-)
 def test_ewma_with_times_variable_spacing(tz_aware_fixture, unit):
     tz = tz_aware_fixture
     halflife = "23 days"