BUG: can't resample with non-nano dateindex, out-of-nanosecond-bounds

[MarcoGorelli]

Pandas version checks

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

idx = pd.date_range('0300-01-01', '2000-01-01', unit='s')
ser = Series(np.ones(len(idx)), index=idx)
ser.resample('D').mean()

Issue Description

---------------------------------------------------------------------------
OverflowError                             Traceback (most recent call last)
File ~/pandas-dev/pandas/_libs/tslibs/timestamps.pyx:1073, in pandas._libs.tslibs.timestamps._Timestamp._as_creso()
   1072 try:
-> 1073     value = convert_reso(self.value, self._creso, creso, round_ok=round_ok)
   1074 except OverflowError as err:

File ~/pandas-dev/pandas/_libs/tslibs/np_datetime.pyx:614, in pandas._libs.tslibs.np_datetime.convert_reso()
    613 # Note: caller is responsible for re-raising as OutOfBoundsTimedelta
--> 614 res_value = value * mult
    615 

OverflowError: value too large

The above exception was the direct cause of the following exception:

OutOfBoundsDatetime                       Traceback (most recent call last)
Cell In[16], line 1
----> 1 ser.resample('D').mean()

File ~/pandas-dev/pandas/core/series.py:5660, in Series.resample(self, rule, axis, closed, label, convention, kind, on, level, origin, offset, group_keys)
   5645 @doc(NDFrame.resample, **_shared_doc_kwargs)  # type: ignore[has-type]
   5646 def resample(
   5647     self,
   (...)
   5658     group_keys: bool | lib.NoDefault = no_default,
   5659 ) -> Resampler:
-> 5660     return super().resample(
   5661         rule=rule,
   5662         axis=axis,
   5663         closed=closed,
   5664         label=label,
   5665         convention=convention,
   5666         kind=kind,
   5667         on=on,
   5668         level=level,
   5669         origin=origin,
   5670         offset=offset,
   5671         group_keys=group_keys,
   5672     )

File ~/pandas-dev/pandas/core/generic.py:8820, in NDFrame.resample(self, rule, axis, closed, label, convention, kind, on, level, origin, offset, group_keys)
   8817 from pandas.core.resample import get_resampler
   8819 axis = self._get_axis_number(axis)
-> 8820 return get_resampler(
   8821     self,
   8822     freq=rule,
   8823     label=label,
   8824     closed=closed,
   8825     axis=axis,
   8826     kind=kind,
   8827     convention=convention,
   8828     key=on,
   8829     level=level,
   8830     origin=origin,
   8831     offset=offset,
   8832     group_keys=group_keys,
   8833 )

File ~/pandas-dev/pandas/core/resample.py:1517, in get_resampler(obj, kind, **kwds)
   1513 """
   1514 Create a TimeGrouper and return our resampler.
   1515 """
   1516 tg = TimeGrouper(**kwds)
-> 1517 return tg._get_resampler(obj, kind=kind)

File ~/pandas-dev/pandas/core/resample.py:1675, in TimeGrouper._get_resampler(self, obj, kind)
   1673 ax = self.ax
   1674 if isinstance(ax, DatetimeIndex):
-> 1675     return DatetimeIndexResampler(
   1676         obj, groupby=self, kind=kind, axis=self.axis, group_keys=self.group_keys
   1677     )
   1678 elif isinstance(ax, PeriodIndex) or kind == "period":
   1679     return PeriodIndexResampler(
   1680         obj, groupby=self, kind=kind, axis=self.axis, group_keys=self.group_keys
   1681     )

File ~/pandas-dev/pandas/core/resample.py:166, in Resampler.__init__(self, obj, groupby, axis, kind, group_keys, selection, **kwargs)
    163 self.as_index = True
    165 self.groupby._set_grouper(self._convert_obj(obj), sort=True)
--> 166 self.binner, self.grouper = self._get_binner()
    167 self._selection = selection
    168 if self.groupby.key is not None:

File ~/pandas-dev/pandas/core/resample.py:253, in Resampler._get_binner(self)
    247 @final
    248 def _get_binner(self):
    249     """
    250     Create the BinGrouper, assume that self.set_grouper(obj)
    251     has already been called.
    252     """
--> 253     binner, bins, binlabels = self._get_binner_for_time()
    254     assert len(bins) == len(binlabels)
    255     bin_grouper = BinGrouper(bins, binlabels, indexer=self.groupby.indexer)

File ~/pandas-dev/pandas/core/resample.py:1249, in DatetimeIndexResampler._get_binner_for_time(self)
   1247 if self.kind == "period":
   1248     return self.groupby._get_time_period_bins(self.ax)
-> 1249 return self.groupby._get_time_bins(self.ax)

File ~/pandas-dev/pandas/core/resample.py:1709, in TimeGrouper._get_time_bins(self, ax)
   1706     binner = labels = DatetimeIndex(data=[], freq=self.freq, name=ax.name)
   1707     return binner, [], labels
-> 1709 first, last = _get_timestamp_range_edges(
   1710     ax.min(),
   1711     ax.max(),
   1712     self.freq,
   1713     closed=self.closed,
   1714     origin=self.origin,
   1715     offset=self.offset,
   1716 )
   1717 # GH #12037
   1718 # use first/last directly instead of call replace() on them
   1719 # because replace() will swallow the nanosecond part
   (...)
   1722 # GH 25758: If DST lands at midnight (e.g. 'America/Havana'), user feedback
   1723 # has noted that ambiguous=True provides the most sensible result
   1724 binner = labels = date_range(
   1725     freq=self.freq,
   1726     start=first,
   (...)
   1731     nonexistent="shift_forward",
   1732 ).as_unit(ax.unit)

File ~/pandas-dev/pandas/core/resample.py:1994, in _get_timestamp_range_edges(first, last, freq, closed, origin, offset)
   1991     if isinstance(origin, Timestamp):
   1992         origin = origin.tz_localize(None)
-> 1994 first, last = _adjust_dates_anchored(
   1995     first, last, freq, closed=closed, origin=origin, offset=offset
   1996 )
   1997 if isinstance(freq, Day):
   1998     first = first.tz_localize(index_tz)

File ~/pandas-dev/pandas/core/resample.py:2101, in _adjust_dates_anchored(first, last, freq, closed, origin, offset)
   2088 def _adjust_dates_anchored(
   2089     first: Timestamp,
   2090     last: Timestamp,
   (...)
   2099     # To handle frequencies that are not multiple or divisible by a day we let
   2100     # the possibility to define a fixed origin timestamp. See GH 31809
-> 2101     first = first.as_unit("ns")
   2102     last = last.as_unit("ns")
   2103     if offset is not None:

File ~/pandas-dev/pandas/_libs/tslibs/timestamps.pyx:1099, in pandas._libs.tslibs.timestamps._Timestamp.as_unit()
   1097 reso = get_unit_from_dtype(dtype)
   1098 try:
-> 1099     return self._as_creso(reso, round_ok=round_ok)
   1100 except OverflowError as err:
   1101     raise OutOfBoundsDatetime(

File ~/pandas-dev/pandas/_libs/tslibs/timestamps.pyx:1076, in pandas._libs.tslibs.timestamps._Timestamp._as_creso()
   1074 except OverflowError as err:
   1075     unit = npy_unit_to_abbrev(creso)
-> 1076     raise OutOfBoundsDatetime(
   1077         f"Cannot cast {self} to unit='{unit}' without overflow."
   1078     ) from err

OutOfBoundsDatetime: Cannot cast 300-01-01 00:00:00 to unit='ns' without overflow.

Expected Behavior

something like

0300-01-01    1.0
0300-01-02    1.0
0300-01-03    1.0
0300-01-04    1.0
0300-01-05    1.0
             ... 
1999-12-28    1.0
1999-12-29    1.0
1999-12-30    1.0
1999-12-31    1.0
2000-01-01    1.0
Freq: D, Length: 109573, dtype: float64

Installed Versions

INSTALLED VERSIONS

commit : 9594c04
python : 3.8.16.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.102.1-microsoft-standard-WSL2
Version : #1 SMP Wed Mar 2 00:30:59 UTC 2022
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8

pandas : 2.0.0.dev0+1336.g9594c04eb7
numpy : 1.23.5
pytz : 2022.7.1
dateutil : 2.8.2
setuptools : 66.1.1
pip : 22.3.1
Cython : 0.29.32
pytest : 7.2.1
hypothesis : 6.64.0
sphinx : 5.3.0
blosc : 1.11.1
feather : None
xlsxwriter : 3.0.7
lxml.etree : 4.9.2
html5lib : 1.1
pymysql : 1.0.2
psycopg2 : 2.9.5
jinja2 : 3.1.2
IPython : 8.8.0
pandas_datareader: None
bs4 : 4.11.1
bottleneck : 1.3.6
brotli :
fastparquet : 2023.1.0
fsspec : 2022.11.0
gcsfs : 2022.11.0
matplotlib : 3.6.3
numba : 0.56.4
numexpr : 2.8.4
odfpy : None
openpyxl : 3.0.10
pandas_gbq : None
pyarrow : 10.0.1
pyreadstat : 1.2.0
pyxlsb : 1.0.10
s3fs : 2022.11.0
scipy : 1.10.0
snappy :
sqlalchemy : 1.4.45
tables : 3.8.0
tabulate : 0.9.0
xarray : 2023.1.0
xlrd : 2.0.1
zstandard : 0.19.0
tzdata : 2022.7
qtpy : None
pyqt5 : None
None

[MarcoGorelli]

similarly for

In [5]: idx = date_range('1900-01-01', '2000-01-01', unit='s')

In [6]: ser = Series(np.ones(len(idx)), index=idx)

In [7]: ser.resample('1000Y').mean()

[jbrockmendel]

What happens if you get rid if "first = first.as_unit('ns')" and same for second?

[MarcoGorelli]

I'll try this out next week, hopefully it's not-too-difficult of a fix