ENH: DTA.to_period support non-nano

pandas/_libs/tslibs/vectorized.pyi

@@ -14,6 +14,7 @@ def dt64arr_to_periodarr(
     stamps: npt.NDArray[np.int64],
     freq: int,
     tz: tzinfo | None,
+    reso: int = ...,  # NPY_DATETIMEUNIT
 ) -> npt.NDArray[np.int64]: ...
 def is_date_array_normalized(
     stamps: npt.NDArray[np.int64],

pandas/_libs/tslibs/vectorized.pyx

@@ -33,6 +33,7 @@ from .np_datetime cimport (
     NPY_FR_ns,
     dt64_to_dtstruct,
     npy_datetimestruct,
+    pandas_datetime_to_datetimestruct,
 )
 from .offsets cimport BaseOffset
 from .period cimport get_period_ordinal
@@ -354,10 +355,12 @@ def is_date_array_normalized(ndarray stamps, tzinfo tz, NPY_DATETIMEUNIT reso) -
 
 @cython.wraparound(False)
 @cython.boundscheck(False)
-def dt64arr_to_periodarr(ndarray stamps, int freq, tzinfo tz):
+def dt64arr_to_periodarr(
+    ndarray stamps, int freq, tzinfo tz, NPY_DATETIMEUNIT reso=NPY_FR_ns
+):
     # stamps is int64_t, arbitrary ndim
     cdef:
-        Localizer info = Localizer(tz, reso=NPY_FR_ns)
+        Localizer info = Localizer(tz, reso=reso)
         Py_ssize_t i, n = stamps.size
         Py_ssize_t pos = -1  # unused, avoid not-initialized warning
         int64_t utc_val, local_val, res_val
@@ -374,7 +377,7 @@ def dt64arr_to_periodarr(ndarray stamps, int freq, tzinfo tz):
             res_val = NPY_NAT
         else:
             local_val = info.utc_val_to_local_val(utc_val, &pos)
-            dt64_to_dtstruct(local_val, &dts)
+            pandas_datetime_to_datetimestruct(local_val, reso, &dts)
             res_val = get_period_ordinal(&dts, freq)
 
         # Analogous to: result[i] = res_val

pandas/core/arrays/period.py

@@ -22,6 +22,7 @@
     astype_overflowsafe,
     delta_to_nanoseconds,
     dt64arr_to_periodarr as c_dt64arr_to_periodarr,
+    get_unit_from_dtype,
     iNaT,
     parsing,
     period as libperiod,
@@ -1024,7 +1025,7 @@ def dt64arr_to_periodarr(data, freq, tz=None):
         used.
 
     """
-    if data.dtype != np.dtype("M8[ns]"):
+    if not isinstance(data.dtype, np.dtype) or data.dtype.kind != "M":
         raise ValueError(f"Wrong dtype: {data.dtype}")
 
     if freq is None:
@@ -1036,9 +1037,10 @@ def dt64arr_to_periodarr(data, freq, tz=None):
     elif isinstance(data, (ABCIndex, ABCSeries)):
         data = data._values
 
+    reso = get_unit_from_dtype(data.dtype)
     freq = Period._maybe_convert_freq(freq)
     base = freq._period_dtype_code
-    return c_dt64arr_to_periodarr(data.view("i8"), base, tz), freq
+    return c_dt64arr_to_periodarr(data.view("i8"), base, tz, reso=reso), freq
 
 
 def _get_ordinal_range(start, end, periods, freq, mult=1):

pandas/tests/arrays/test_datetimes.py

@@ -37,6 +37,26 @@ def dtype(self, unit, tz_naive_fixture):
         else:
             return DatetimeTZDtype(unit=unit, tz=tz)
 
+    @pytest.fixture
+    def dta_dti(self, unit, dtype):
+        tz = getattr(dtype, "tz", None)
+
+        dti = pd.date_range("2016-01-01", periods=55, freq="D", tz=tz)
+        if tz is None:
+            arr = np.asarray(dti).astype(f"M8[{unit}]")
+        else:
+            arr = np.asarray(dti.tz_convert("UTC").tz_localize(None)).astype(
+                f"M8[{unit}]"
+            )
+
+        dta = DatetimeArray._simple_new(arr, dtype=dtype)
+        return dta, dti
+
+    @pytest.fixture
+    def dta(self, dta_dti):
+        dta, dti = dta_dti
+        return dta
+
     def test_non_nano(self, unit, reso, dtype):
         arr = np.arange(5, dtype=np.int64).view(f"M8[{unit}]")
         dta = DatetimeArray._simple_new(arr, dtype=dtype)
@@ -52,17 +72,8 @@ def test_non_nano(self, unit, reso, dtype):
     @pytest.mark.parametrize(
         "field", DatetimeArray._field_ops + DatetimeArray._bool_ops
     )
-    def test_fields(self, unit, reso, field, dtype):
-        tz = getattr(dtype, "tz", None)
-        dti = pd.date_range("2016-01-01", periods=55, freq="D", tz=tz)
-        if tz is None:
-            arr = np.asarray(dti).astype(f"M8[{unit}]")
-        else:
-            arr = np.asarray(dti.tz_convert("UTC").tz_localize(None)).astype(
-                f"M8[{unit}]"
-            )
-
-        dta = DatetimeArray._simple_new(arr, dtype=dtype)
+    def test_fields(self, unit, reso, field, dtype, dta_dti):
+        dta, dti = dta_dti
 
         # FIXME: assert (dti == dta).all()
 
@@ -107,6 +118,14 @@ def test_std_non_nano(self, unit):
         assert res._reso == dta._reso
         assert res == dti.std().floor(unit)
 
+    @pytest.mark.filterwarnings("ignore:Converting to PeriodArray.*:UserWarning")
+    def test_to_period(self, dta_dti):
+        dta, dti = dta_dti
+        result = dta.to_period("D")
+        expected = dti._data.to_period("D")
+
+        tm.assert_extension_array_equal(result, expected)
+
 
 class TestDatetimeArrayComparisons:
     # TODO: merge this into tests/arithmetic/test_datetime64 once it is

pandas/tests/indexes/period/test_constructors.py

@@ -183,9 +183,10 @@ def test_constructor_datetime64arr(self):
         vals = np.arange(100000, 100000 + 10000, 100, dtype=np.int64)
         vals = vals.view(np.dtype("M8[us]"))
 
-        msg = r"Wrong dtype: datetime64\[us\]"
-        with pytest.raises(ValueError, match=msg):
-            PeriodIndex(vals, freq="D")
+        pi = PeriodIndex(vals, freq="D")
+
+        expected = PeriodIndex(vals.astype("M8[ns]"), freq="D")
+        tm.assert_index_equal(pi, expected)
 
     @pytest.mark.parametrize("box", [None, "series", "index"])
     def test_constructor_datetime64arr_ok(self, box):

setup.py

@@ -551,7 +551,11 @@ def srcpath(name=None, suffix=".pyx", subdir="src"):
         "depends": tseries_depends,
         "sources": ["pandas/_libs/tslibs/src/datetime/np_datetime.c"],
     },
-    "_libs.tslibs.vectorized": {"pyxfile": "_libs/tslibs/vectorized"},
+    "_libs.tslibs.vectorized": {
+        "pyxfile": "_libs/tslibs/vectorized",
+        "depends": tseries_depends,
+        "sources": ["pandas/_libs/tslibs/src/datetime/np_datetime.c"],
+    },
     "_libs.testing": {"pyxfile": "_libs/testing"},
     "_libs.window.aggregations": {
         "pyxfile": "_libs/window/aggregations",