Description
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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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(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()
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