BUG: calendar day date_range AmbiguousTimeError with unambiguous DST boundaries #56240
Description
Pandas version checks
-
I have checked that this issue has not already been reported.
-
I have confirmed this bug exists on the latest version of pandas.
-
I have confirmed this bug exists on the main branch of pandas.
Reproducible Example
import pandas as pd
start = pd.Timestamp("2023-11-05T01:00:00-07:00")
start = start.tz_convert("America/Los_Angeles")
end = start + pd.DateOffset(hours=1)
# Works fine
pd.date_range(start, end, freq='H')
# Also works fine
pd.date_range(start, end, freq='24H') # 23/25H works too
# Fails: both 'start' and 'end' are DST folds
pd.date_range(start, end, freq='D')
# Fails: 'start' is a DST fold
pd.date_range(start, end + pd.DateOffset(days=1), freq='D')
# Fails: 'end' is a DST fold
pd.date_range(start - pd.DateOffset(days=1), end, freq='D')
# Fails: the calendar day range contains a DST fold (the actually ambiguous case)
pd.date_range(start - pd.DateOffset(days=1), end + pd.DateOffset(days=1), freq='D')Issue Description
date_range returns an AmbiguousTimeError when its calendar day range contains a DST boundary. It seems to happen whenever the output would contain a DST boundary, even if the inputs are time zone aware in the same time zone.
Expected Behavior
The returned range(s) acknowledge the unambiguous DST folds, e.g.
>>> pd.date_range(start, end, freq='D') # Both time zone aware DST folds
... # Just 'start' in the rangeInstalled Versions
INSTALLED VERSIONS
commit : 66e3805
python : 3.7.17.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.102.1-microsoft-standard-WSL2
Version : #1 SMP Wed Mar 2 00:30:59 UTC 2022
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.5
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.2
pip : 23.3.1
setuptools : 47.1.0
Cython : None
pytest : 7.1.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 3.0.9
lxml.etree : 4.4.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2023.1.0
fastparquet : None
gcsfs : None
matplotlib : 3.5.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 10.0.1
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 2.0.6
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None