Description
Pandas version checks
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I have confirmed this bug exists on the latest version of pandas.
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I have confirmed this bug exists on the main branch of pandas.
Reproducible Example
import pandas as pd
series = pd.Series(pd.to_datetime(["2023-04-05T00:00:00"]), dtype="timestamp[ns][pyarrow]")
series.dt.strftime("%Y-%m-%dT%H:%M:%S")OUTPUT:
2023-04-05T00:00:00.000000000
Issue Description
I've started using Pandas (2.1.0) with the PyArrow backend (e.g. for read_sql) and it's currently breaking my code by returning seconds together with their fractional part when using .dt.strftime("%Y-%m-%dT%H:%M:%S").
Apparently, this is the expected behavior by looking at the PyArrow strftime documentation, but at the same time pandas documentation refers to the Python string format docs in their strftime documentation (which allow for a separate format code for the fractional part, %f).
This can be mitigated by casting the timestamps to timestamp[s][pyarrow] (second precision). However, timestamps/datetime get automatically casted to timestamp[ns][pyarrow] (nanosecond precision) when, for example, reading from a SQL database and that's quite convenient.
I thought this could be considered to be a bug in Pandas (especially considering that Pandas wants to increase their reliance on PyArrow by making it a mandatory requirement in 3.x) or at least the documentation would benefit from a clarification.
Expected Behavior
I'd expect pandas' strftime to keep returning strings exclusively reporting non-fractional seconds when using the %S format code, just as when using standard numpy dtypes:
import pandas as pd
series = pd.Series(pd.to_datetime(["2023-04-05T00:00:00"]))
series.dt.strftime("%Y-%m-%dT%H:%M:%S")OUTPUT: 2023-04-05T00:00:00
Installed Versions
INSTALLED VERSIONS
commit : ba1cccd
python : 3.11.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.14.0-1054-oem
Version : #61-Ubuntu SMP Fri Oct 14 13:05:50 UTC 2022
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.1.0
numpy : 1.24.1
pytz : 2022.7
dateutil : 2.8.2
setuptools : 68.1.2
pip : 23.1.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.1
pymysql : None
psycopg2 : 2.9.5
jinja2 : 3.1.2
IPython : 8.11.0
pandas_datareader : None
bs4 : 4.9.3
bottleneck : 1.3.7
dataframe-api-compat: None
fastparquet : None
fsspec : 2022.11.0
gcsfs : None
matplotlib : 3.7.1
numba : 0.57.1
numexpr : 2.8.5
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : 13.0.0
pyreadstat : None
pyxlsb : None
s3fs : 2022.11.0
scipy : 1.11.2
sqlalchemy : 1.4.49
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None