BUG: astype, asfreq with non-nano (#55958)

* BUG: period.astype to non-nano dt64

* BUG: .asfreq with non-nano

* GH ref

Author: jbrockmendel

doc/source/whatsnew/v2.2.0.rst

@@ -421,6 +421,7 @@ I/O
 Period
 ^^^^^^
 - Bug in :class:`Period` addition silently wrapping around instead of raising ``OverflowError`` (:issue:`55503`)
+- Bug in casting from :class:`PeriodDtype` with ``astype`` to ``datetime64`` or :class:`DatetimeTZDtype` with non-nanosecond unit incorrectly returning with nanosecond unit (:issue:`55958`)
 -
 
 Plotting
@@ -433,6 +434,7 @@ Groupby/resample/rolling
 - Bug in :class:`.Rolling` where duplicate datetimelike indexes are treated as consecutive rather than equal with ``closed='left'`` and ``closed='neither'`` (:issue:`20712`)
 - 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.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`)

pandas/core/arrays/period.py

@@ -784,7 +784,8 @@ def astype(self, dtype, copy: bool = True):
         if lib.is_np_dtype(dtype, "M") or isinstance(dtype, DatetimeTZDtype):
             # GH#45038 match PeriodIndex behavior.
             tz = getattr(dtype, "tz", None)
-            return self.to_timestamp().tz_localize(tz)
+            unit = dtl.dtype_to_unit(dtype)
+            return self.to_timestamp().tz_localize(tz).as_unit(unit)
 
         return super().astype(dtype, copy=copy)
 

pandas/core/resample.py

@@ -2782,7 +2782,11 @@ def asfreq(
 
         new_obj.index = _asfreq_compat(obj.index, freq)
     else:
-        dti = date_range(obj.index.min(), obj.index.max(), freq=freq)
+        unit = None
+        if isinstance(obj.index, DatetimeIndex):
+            # TODO: should we disallow non-DatetimeIndex?
+            unit = obj.index.unit
+        dti = date_range(obj.index.min(), obj.index.max(), freq=freq, unit=unit)
         dti.name = obj.index.name
         new_obj = obj.reindex(dti, method=method, fill_value=fill_value)
         if normalize:

pandas/tests/indexes/period/methods/test_astype.py

@@ -139,10 +139,13 @@ def test_astype_array_fallback(self):
         expected = np.array([True, True])
         tm.assert_numpy_array_equal(result, expected)
 
-    def test_period_astype_to_timestamp(self):
+    def test_period_astype_to_timestamp(self, unit):
+        # GH#55958
         pi = PeriodIndex(["2011-01", "2011-02", "2011-03"], freq="M")
 
-        exp = DatetimeIndex(["2011-01-01", "2011-02-01", "2011-03-01"], tz="US/Eastern")
-        res = pi.astype("datetime64[ns, US/Eastern]")
+        exp = DatetimeIndex(
+            ["2011-01-01", "2011-02-01", "2011-03-01"], tz="US/Eastern"
+        ).as_unit(unit)
+        res = pi.astype(f"datetime64[{unit}, US/Eastern]")
         tm.assert_index_equal(res, exp)
         assert res.freq == exp.freq

pandas/tests/resample/test_datetime_index.py

@@ -1267,10 +1267,14 @@ def test_resample_not_monotonic(unit):
         ),
     ],
 )
-def test_resample_median_bug_1688(dtype):
+def test_resample_median_bug_1688(dtype, unit):
+    # GH#55958
+    dti = DatetimeIndex(
+        [datetime(2012, 1, 1, 0, 0, 0), datetime(2012, 1, 1, 0, 5, 0)]
+    ).as_unit(unit)
     df = DataFrame(
         [1, 2],
-        index=[datetime(2012, 1, 1, 0, 0, 0), datetime(2012, 1, 1, 0, 5, 0)],
+        index=dti,
         dtype=dtype,
     )