ENH: get_resolution support non-nano

pandas/_libs/tslibs/vectorized.pyi

@@ -28,6 +28,7 @@ def normalize_i8_timestamps(
 def get_resolution(
     stamps: npt.NDArray[np.int64],
     tz: tzinfo | None = ...,
+    reso: int = ...,  # NPY_DATETIMEUNIT
 ) -> Resolution: ...
 def ints_to_pydatetime(
     arr: 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
@@ -226,17 +227,19 @@ cdef inline c_Resolution _reso_stamp(npy_datetimestruct *dts):
 
 @cython.wraparound(False)
 @cython.boundscheck(False)
-def get_resolution(ndarray stamps, tzinfo tz=None) -> Resolution:
+def get_resolution(
+    ndarray stamps, tzinfo tz=None, NPY_DATETIMEUNIT reso=NPY_FR_ns
+) -> Resolution:
     # stamps is int64_t, any ndim
     cdef:
-        Localizer info = Localizer(tz, reso=NPY_FR_ns)
+        Localizer info = Localizer(tz, reso=reso)
         int64_t utc_val, local_val
         Py_ssize_t i, n = stamps.size
         Py_ssize_t pos = -1  # unused, avoid not-initialized warning
         cnp.flatiter it = cnp.PyArray_IterNew(stamps)
 
         npy_datetimestruct dts
-        c_Resolution reso = c_Resolution.RESO_DAY, curr_reso
+        c_Resolution pd_reso = c_Resolution.RESO_DAY, curr_reso
 
     for i in range(n):
         # Analogous to: utc_val = stamps[i]
@@ -247,14 +250,14 @@ def get_resolution(ndarray stamps, tzinfo tz=None) -> Resolution:
         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)
             curr_reso = _reso_stamp(&dts)
-            if curr_reso < reso:
-                reso = curr_reso
+            if curr_reso < pd_reso:
+                pd_reso = curr_reso
 
         cnp.PyArray_ITER_NEXT(it)
 
-    return Resolution(reso)
+    return Resolution(pd_reso)
 
 
 # -------------------------------------------------------------------------

pandas/core/arrays/datetimes.py

@@ -619,7 +619,7 @@ def is_normalized(self) -> bool:
 
     @property  # NB: override with cache_readonly in immutable subclasses
     def _resolution_obj(self) -> Resolution:
-        return get_resolution(self.asi8, self.tz)
+        return get_resolution(self.asi8, self.tz, reso=self._reso)
 
     # ----------------------------------------------------------------
     # Array-Like / EA-Interface Methods

pandas/tests/tslibs/test_resolution.py

@@ -1,13 +1,24 @@
 import numpy as np
+import pytz
 
 from pandas._libs.tslibs import (
     Resolution,
     get_resolution,
 )
+from pandas._libs.tslibs.dtypes import NpyDatetimeUnit
 
 
 def test_get_resolution_nano():
     # don't return the fallback RESO_DAY
     arr = np.array([1], dtype=np.int64)
     res = get_resolution(arr)
     assert res == Resolution.RESO_NS
+
+
+def test_get_resolution_non_nano_data():
+    arr = np.array([1], dtype=np.int64)
+    res = get_resolution(arr, None, NpyDatetimeUnit.NPY_FR_us.value)
+    assert res == Resolution.RESO_US
+
+    res = get_resolution(arr, pytz.UTC, NpyDatetimeUnit.NPY_FR_us.value)
+    assert res == Resolution.RESO_US

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",