Description
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
l1 = pd.DatetimeIndex(['2024-05-11', '2024-05-12'], dtype='datetime64[us]', name='Date', freq='D')
l2 = pd.DatetimeIndex(['2024-05-13'], dtype='datetime64[us]', name='Date', freq='D')
print(l1.union(l2))
# Returns DatetimeIndex(['2024-05-11', '2024-05-13', '2027-02-05'], dtype='datetime64[us]', name='Date', freq='D')Issue Description
DatetimeIndex.union is returning an incorrect result. Since this method is used by MultiIndex.concat, it leads to unexpected errors when combining several MultiIndex DataFrames/Series that have a datetime64[us] level.
Expected Behavior
DatetimeIndex(['2024-05-11', '2024-05-12', '2024-05-13'], dtype='datetime64[us]', name='Date', freq=None)
Installed Versions
INSTALLED VERSIONS
commit : d9cdd2e
python : 3.11.9.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.22631
machine : AMD64
processor : Intel64 Family 6 Model 141 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 2.2.2
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0
setuptools : 69.5.1
pip : 24.0
Cython : None
pytest : 7.4.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 3.2.0
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.4
IPython : 8.25.0
pandas_datareader : None
adbc-driver-sqlite : None
bs4 : None
bottleneck : 1.3.8
dataframe-api-compat : None
fastparquet : None
fsspec : 2024.6.0
gcsfs : None
matplotlib : None
numba : 0.59.1
numexpr : 2.10.0
odfpy : None
openpyxl : 3.1.3
pandas_gbq : None
pyarrow : 16.1.0
pyreadstat : 1.2.7
python-calamine : None
pyxlsb : 1.0.10
s3fs : None
scipy : None
sqlalchemy : 2.0.30
tables : None
tabulate : 0.9.0
xarray : None
xlrd : 2.0.1
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None