Integer overflow in Series.astype('datetime64[D]') #28045
Description
Code Sample, a copy-pastable example if possible
>>> s = pd.Series(['9/22/1677', '4/11/2262'], dtype="U") >>> s 0 9/22/1677 1 4/11/2262 dtype: object >>> s.astype('datetime64') 0 1677-09-22 1 2262-04-11 dtype: datetime64[ns] >>> s.astype('datetime64[s]') 0 1677-09-22 1 2262-04-11 dtype: datetime64[ns] >>> s.astype('datetime64[m]') 0 1677-09-22 1 2262-04-11 dtype: datetime64[ns] >>> s.astype('datetime64[h]') 0 1677-09-22 1 2262-04-11 dtype: datetime64[ns] >>> s.astype('datetime64[D]') 0 2262-04-11 # <<<< wait, what? 1 2262-04-11 dtype: datetime64[ns] Problem description
It appears that astype('datetime64[D]') breaks on the earliest representable full day -- it wraps around to the latest representable date.
While I know this is super edge case-y and highly unlikely to be worth a lot of time, the fact that it wraps around is indicative of a potential for integer overflow somewhere that may or may not bite us in the future.
Expected Output
.astype('datetime64[D]') should give 1677-09-22 instead of 2262-04-11.
Output of pd.show_versions()
INSTALLED VERSIONS
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.1.0
Cython : 0.29.13
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.14.1
pytables : None
s3fs : 0.3.3
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None