Description
Pandas version checks
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I have checked that this issue has not already been reported.
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I have confirmed this bug exists on the latest version of pandas.
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I have confirmed this bug exists on the main branch of pandas.
Reproducible Example
>>> a = pd.Series([1666880195890293744, 5]).to_frame()
>>> b = pd.Series([6, 7, 8]).to_frame()
>>> a
0
0 1666880195890293744
1 5
>>> b
0
0 6
1 7
2 8
>>> a.combine_first(b)
0
0 1666880195890293760
1 5
2 8Issue Description
I tried this on Pandas version 2.2.2 and I see that there is a loss of precision. This could be related to issue #51777.
Expected Behavior
1666880195890293744 should not get changed to 1666880195890293760
Installed Versions
INSTALLED VERSIONS
commit : d9cdd2e
python : 3.10.9.final.0
python-bits : 64
OS : Darwin
OS-release : 24.0.0
Version : Darwin Kernel Version 24.0.0: Tue Sep 24 23:37:36 PDT 2024; root:xnu-11215.1.12~1/RELEASE_ARM64_T6020
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.2.2
numpy : 1.26.4
pytz : 2022.7.1
dateutil : 2.8.2
setuptools : 67.3.2
pip : 24.2
Cython : 0.29.33
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.9.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.12.0
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.0
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.6.3
numba : None
numexpr : 2.8.4
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.10.0
sqlalchemy : None
tables : 3.8.0
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None