BUG:unexpected behavior of pandas 1.1.1 dt.normalize() on pre-epoch dates · Issue #36294 · pandas-dev/pandas (original) (raw)

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• I have confirmed this bug exists on the latest version of pandas.
• (optional) I have confirmed this bug exists on the master branch of pandas.

Code Sample, a copy-pastable example

import pandas as pd print(pd.version) 1.1.1

df = pd.DataFrame({'year': [1969, 2016],'month': [1, 1],'day': [1, 1],'hour': [9, 9]}) df=pd.to_datetime(df)

df

Out[10]: 0 1969-01-01 09:00:00 1 2016-01-01 09:00:00 dtype: datetime64[ns]

df.dt.normalize()

Out[11]: 0 1969-01-02 1 2016-01-01 dtype: datetime64[ns]

Problem description

I would expect pre-epoch behavior to be the same as post-epoch

Expected Output

Out[10]: 
0   1969-01-01 09:00:00
1   2016-01-01 09:00:00
dtype: datetime64[ns]

df.dt.normalize()

Out[11]: 
0   1969-01-01
1   2016-01-01
dtype: datetime64[ns]

Output of pd.show_versions()

commit : f2ca0a2python : 3.6.10.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.17763 machine : AMD64 processor : Intel64 Family 6 Model 85 Stepping 4, GenuineIntel byteorder : little LC_ALL : None LANG : en LOCALE : None.None

pandas : 1.1.1
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
Cython : 0.29.21
pytest : 6.0.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.3
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.8.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.19
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.50.1