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BUG: Fix dt64[non_nano] + some_offsets incorrectly rounding #62383

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BUG: Fix dt64[non_nano] + some_offsets incorrectly rounding #62383

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184ad31
BUG: Fix dt64[non_nano] + offset rounding
Aniketsy Sep 19, 2025
0f088f5
BUG: Fix dt64[non_nano] + offset rounding
Aniketsy Oct 4, 2025
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24 changes: 23 additions & 1 deletion pandas/core/arrays/datetimes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -817,7 +817,29 @@ def _add_offset(self, offset: BaseOffset) -> Self:
result = type(self)._from_sequence(res_values, dtype=self.dtype)

else:
result = type(self)._simple_new(res_values, dtype=res_values.dtype)
units = [
"ns",
"us",
"ms",
"s",
]
res_unit = self.unit
# Only try to adjust unit if both units are recognized
try:
if hasattr(offset, "offset"):
offset_td = Timedelta(offset.offset)
offset_unit = offset_td.unit
if self.unit in units and offset_unit in units:
idx_self = units.index(self.unit)
idx_offset = units.index(offset_unit)
res_unit = units[min(idx_self, idx_offset)]
except Exception:
res_unit = self.unit
dtype = tz_to_dtype(self.tz, unit=res_unit)
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wont you need to cast res_values before calling simple_new?

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@Aniketsy Aniketsy Oct 4, 2025 •
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yes, thanks! . Should we wrap this in try/except block?

if res_values.dtype != f"datetime64[{res_unit}]":
res_values = res_values.astype(f"datetime64[{res_unit}]")
result = type(self)._simple_new(res_values, dtype=dtype)

if offset.normalize:
result = result.normalize()
result._freq = None
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17 changes: 17 additions & 0 deletions pandas/tests/arrays/test_datetimes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -844,3 +844,20 @@ def test_factorize_sort_without_freq():
tda = dta - dta[0]
with pytest.raises(NotImplementedError, match=msg):
tda.factorize(sort=True)


def test_dt64_non_nano_offset_no_rounding():
# GH#56586
dti = pd.date_range("2016-01-01", periods=3, unit="s")
offset = pd.offsets.CustomBusinessDay(offset=pd.Timedelta("1ms"))
result = dti + offset

assert result.dtype == np.dtype("datetime64[ms]")
expected = pd.DatetimeIndex(
[
pd.Timestamp("2016-01-02 00:00:00.001"),
pd.Timestamp("2016-01-03 00:00:00.001"),
pd.Timestamp("2016-01-04 00:00:00.001"),
]
)
tm.assert_index_equal(result, expected)
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