Pandas version checks
Reproducible Example
import pandas as pd
# Single timestamp
raw = "2023-10-15T14:30:00"
single = pd.to_datetime(raw, utc=True, format="ISO8601")
print(single)
# Output: 2023-10-15 14:30:00+00:00 (correct)
# Series of timestamps
series = pd.Series([0, 0], index=["2023-10-15T10:30:00-12:00", raw])
converted = pd.to_datetime(series.index, utc=True, format="ISO8601")
print(converted)
# Output: 2023-10-16 02:30:00+00:00 for the second one (incorrect)
# error depends on the previous one timezone
Issue Description
When using pd.to_datetime to parse a Series of timestamps with format="ISO8601" and utc=True, the parsing of a timestamp without an explicit timezone offset is incorrect and appears to depend on the timezone offset of the previous timestamp in the Series. This behavior does not occur when parsing a single timestamp.
Expected Behavior
In this configuration, behavior should not depend on the previous timestamp timezone. Result should be the same as when individually passed.
Installed Versions
Details
INSTALLED VERSIONS
commit : 0691c5c
python : 3.10.0
python-bits : 64
OS : Linux
OS-release : 5.10.0-34-amd64
Version : #1 SMP Debian 5.10.234-1 (2025-02-24)
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.2.3
numpy : 1.26.4
pytz : 2024.2
dateutil : 2.9.0.post0
pip : 24.2
Cython : None
sphinx : None
IPython : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : 3.1.6
lxml.etree : None
matplotlib : 3.10.1
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : 15.0.2
pyreadstat : None
pytest : 8.3.5
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.15.2
sqlalchemy : 2.0.40
tables : None
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2025.2
qtpy : None
pyqt5 : None
Traceback (most recent call last):
File "", line 1, in
NameError: name 'version' is not defined
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
Issue Description
When using pd.to_datetime to parse a Series of timestamps with format="ISO8601" and utc=True, the parsing of a timestamp without an explicit timezone offset is incorrect and appears to depend on the timezone offset of the previous timestamp in the Series. This behavior does not occur when parsing a single timestamp.
Expected Behavior
In this configuration, behavior should not depend on the previous timestamp timezone. Result should be the same as when individually passed.
Installed Versions
Details
INSTALLED VERSIONS
commit : 0691c5c
python : 3.10.0
python-bits : 64
OS : Linux
OS-release : 5.10.0-34-amd64
Version : #1 SMP Debian 5.10.234-1 (2025-02-24)
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.2.3
numpy : 1.26.4
pytz : 2024.2
dateutil : 2.9.0.post0
pip : 24.2
Cython : None
sphinx : None
IPython : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : 3.1.6
lxml.etree : None
matplotlib : 3.10.1
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : 15.0.2
pyreadstat : None
pytest : 8.3.5
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.15.2
sqlalchemy : 2.0.40
tables : None
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2025.2
qtpy : None
pyqt5 : None
Traceback (most recent call last):
File "", line 1, in
NameError: name 'version' is not defined