OutOfBoundsDatetime for big-endian np.datetime64 arrays · Issue #29684 · pandas-dev/pandas (original) (raw)

Code Sample, a copy-pastable example if possible

ipdb> value = np.array([np.datetime64(1, 'ms')], dtype="<M8[ms]") ipdb> pd.Series(value) 0 1970-01-01 00:00:00.001 dtype: datetime64[ns] ipdb> value = np.array([np.datetime64(1, 'ms')], dtype=">M8[ms]") ipdb> pd.Series(value) *** pandas._libs.tslibs.np_datetime.OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 2285384-04-02 23:52:07

Problem description

Seems like something weird is going on when converting the big-endian timestamp, which does not happen for the little-endian one.

Expected Output

The same as for the little-endian data type.

Output of pd.show_versions()

[paste the output of pd.show_versions() here below this line]

ipdb> pd.show_versions()

INSTALLED VERSIONS

commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 18.5.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 0.25.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.6.0.post20191101
Cython : None
pytest : 5.0.1
hypothesis : 4.44.2
sphinx : 2.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None