BUG: round with non-nanosecond raising OverflowError (#56158)

* BUG: round with non-nanosecond raising OverflowError

* GH ref

* woops

Author: jbrockmendel

doc/source/whatsnew/v2.2.0.rst

@@ -453,6 +453,7 @@ Datetimelike
 - Bug in :meth:`DatetimeIndex.union` returning object dtype for tz-aware indexes with the same timezone but different units (:issue:`55238`)
 - Bug in :meth:`Index.is_monotonic_increasing` and :meth:`Index.is_monotonic_decreasing` always caching :meth:`Index.is_unique` as ``True`` when first value in index is ``NaT`` (:issue:`55755`)
 - Bug in :meth:`Index.view` to a datetime64 dtype with non-supported resolution incorrectly raising (:issue:`55710`)
+- Bug in :meth:`Series.dt.round` with non-nanosecond resolution and ``NaT`` entries incorrectly raising ``OverflowError`` (:issue:`56158`)
 - Bug in :meth:`Tick.delta` with very large ticks raising ``OverflowError`` instead of ``OutOfBoundsTimedelta`` (:issue:`55503`)
 - Bug in ``.astype`` converting from a higher-resolution ``datetime64`` dtype to a lower-resolution ``datetime64`` dtype (e.g. ``datetime64[us]->datetim64[ms]``) silently overflowing with values near the lower implementation bound (:issue:`55979`)
 - Bug in adding or subtracting a :class:`Week` offset to a ``datetime64`` :class:`Series`, :class:`Index`, or :class:`DataFrame` column with non-nanosecond resolution returning incorrect results (:issue:`55583`)

pandas/_libs/tslibs/fields.pyx

@@ -746,7 +746,31 @@ cdef ndarray[int64_t] _ceil_int64(const int64_t[:] values, int64_t unit):
 
 
 cdef ndarray[int64_t] _rounddown_int64(values, int64_t unit):
-    return _ceil_int64(values - unit // 2, unit)
+    cdef:
+        Py_ssize_t i, n = len(values)
+        ndarray[int64_t] result = np.empty(n, dtype="i8")
+        int64_t res, value, remainder, half
+
+    half = unit // 2
+
+    with cython.overflowcheck(True):
+        for i in range(n):
+            value = values[i]
+
+            if value == NPY_NAT:
+                res = NPY_NAT
+            else:
+                # This adjustment is the only difference between rounddown_int64
+                #  and _ceil_int64
+                value = value - half
+                remainder = value % unit
+                if remainder == 0:
+                    res = value
+                else:
+                    res = value + (unit - remainder)
+
+            result[i] = res
+    return result
 
 
 cdef ndarray[int64_t] _roundup_int64(values, int64_t unit):

pandas/tests/series/methods/test_round.py

@@ -56,9 +56,10 @@ def test_round_builtin(self, any_float_dtype):
 
     @pytest.mark.parametrize("method", ["round", "floor", "ceil"])
     @pytest.mark.parametrize("freq", ["s", "5s", "min", "5min", "h", "5h"])
-    def test_round_nat(self, method, freq):
-        # GH14940
-        ser = Series([pd.NaT])
-        expected = Series(pd.NaT)
+    def test_round_nat(self, method, freq, unit):
+        # GH14940, GH#56158
+        ser = Series([pd.NaT], dtype=f"M8[{unit}]")
+        expected = Series(pd.NaT, dtype=f"M8[{unit}]")
         round_method = getattr(ser.dt, method)
-        tm.assert_series_equal(round_method(freq), expected)
+        result = round_method(freq)
+        tm.assert_series_equal(result, expected)