Description
Pandas version checks
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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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I have confirmed this bug exists on the main branch of pandas.
Reproducible Example
import datetime as dt
import pandas as pd
pd.Timestamp(2020, 1, 1, 0, 0, tzinfo=dt.timezone.utc, fold=0)
Issue Description
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/_libs/tslibs/timestamps.pyx", line 1584, in pandas._libs.tslibs.timestamps.Timestamp.__new__
ValueError: Cannot pass fold with possibly unambiguous input: int, float, numpy.datetime64, str, or timezone-aware datetime-like. Pass naive datetime-like or build Timestamp from components.
Code works with Pandas 1.5. Possible Pandas 2.0 regression?
Search yielded #44378 except I do build from components.
When passing components as kwargs, it works:
pd.Timestamp(year=2020, month=1, day=1, hour=0, minute=0, tzinfo=dt.timezone.utc, fold=0)
Timestamp('2020-01-01 00:00:00+0000', tz='UTC')
I figured maybe I'm not using the constructor correctly but I couldn't find a mention in the docs preventing this usage.
The only test I found regarding this is this one:
pandas/pandas/tests/scalar/timestamp/test_constructors.py
Lines 839 to 847 in ee84ef2
It is this test that led me to the "pass all as kwargs" solution.
Is this a bug or a known limitation?
Expected Behavior
Timestamp('2020-01-01 00:00:00+0000', tz='UTC')
Installed Versions
INSTALLED VERSIONS
commit : c2a7f1a
python : 3.9.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.0-21-amd64
Version : #1 SMP Debian 5.10.162-1 (2023-01-21)
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : fr_FR.UTF-8
pandas : 2.0.0rc1
numpy : 1.24.2
pytz : 2022.7.1
dateutil : 2.8.2
setuptools : 65.3.0
pip : 23.0.1
Cython : None
pytest : 7.2.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.9.5
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.10.1
snappy : None
sqlalchemy : 2.0.4
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : None
qtpy : None
pyqt5 : None