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.
In [1]: to_datetime([Timestamp('2020').tz_localize('Europe/London'), Timestamp('2020').tz_localize('US/Pacific')], errors='ignore')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[1], line 1
----> 1 to_datetime([Timestamp('2020').tz_localize('Europe/London'), Timestamp('2020').tz_localize('US/Pacific')], errors='ignore')
File ~/pandas-dev/pandas/core/tools/datetimes.py:1063, in to_datetime(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)
1061 result = _convert_and_box_cache(argc, cache_array)
1062 else:
-> 1063 result = convert_listlike(argc, format)
1064 else:
1065 result = convert_listlike(np.array([arg]), format)[0]
File ~/pandas-dev/pandas/core/tools/datetimes.py:437, in _convert_listlike_datetimes(arg, format, name, utc, unit, errors, dayfirst, yearfirst, exact)
434 if format is not None:
435 return _array_strptime_with_fallback(arg, name, utc, format, exact, errors)
--> 437 result, tz_parsed = objects_to_datetime64ns(
438 arg,
439 dayfirst=dayfirst,
440 yearfirst=yearfirst,
441 utc=utc,
442 errors=errors,
443 allow_object=True,
444 )
446 if tz_parsed is not None:
447 # We can take a shortcut since the datetime64 numpy array
448 # is in UTC
449 dta = DatetimeArray(result, dtype=tz_to_dtype(tz_parsed))
File ~/pandas-dev/pandas/core/arrays/datetimes.py:2158, in objects_to_datetime64ns(data, dayfirst, yearfirst, utc, errors, allow_object)
2156 order: Literal["F", "C"] = "F" if flags.f_contiguous else "C"
2157 try:
-> 2158 result, tz_parsed = tslib.array_to_datetime(
2159 data.ravel("K"),
2160 errors=errors,
2161 utc=utc,
2162 dayfirst=dayfirst,
2163 yearfirst=yearfirst,
2164 )
2165 result = result.reshape(data.shape, order=order)
2166 except OverflowError as err:
2167 # Exception is raised when a part of date is greater than 32 bit signed int
File ~/pandas-dev/pandas/_libs/tslib.pyx:441, in pandas._libs.tslib.array_to_datetime()
439 @cython.wraparound(False)
440 @cython.boundscheck(False)
--> 441 cpdef array_to_datetime(
442 ndarray[object] values,
443 str errors="raise",
File ~/pandas-dev/pandas/_libs/tslib.pyx:521, in pandas._libs.tslib.array_to_datetime()
519 else:
520 found_naive = True
--> 521 tz_out = convert_timezone(
522 val.tzinfo,
523 tz_out,
File ~/pandas-dev/pandas/_libs/tslibs/conversion.pyx:725, in pandas._libs.tslibs.conversion.convert_timezone()
723 "datetime64 unless utc=True")
724 elif tz_out is not None and not tz_compare(tz_out, tz_in):
--> 725 raise ValueError("Tz-aware datetime.datetime "
726 "cannot be converted to "
727 "datetime64 unless utc=True")
ValueError: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True
In [2]: to_datetime([Timestamp('2020').tz_localize('Europe/London'), Timestamp('2020').tz_localize('US/Pacific')], errors='ignore', format='%Y-%m
...: -%d')
Out[2]: Index([2020-01-01 00:00:00+00:00, 2020-01-01 00:00:00-08:00], dtype='object') Issue Description
It shouldn't raise with errors='ignore'
Index([2020-01-01 00:00:00+00:00, 2020-01-01 00:00:00-08:00], dtype='object')
Installed Versions INSTALLED VERSIONScommit : 8acd314
python : 3.8.16.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 : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 2.0.0.dev0+1049.g8acd31495a
numpy : 1.23.5
pytz : 2022.7
dateutil : 2.8.2
setuptools : 65.6.3
pip : 22.3.1
Cython : 0.29.32
pytest : 7.2.0
hypothesis : 6.61.0
sphinx : 5.3.0
blosc : 1.11.1
feather : None
xlsxwriter : 3.0.6
lxml.etree : 4.9.2
html5lib : 1.1
pymysql : 1.0.2
psycopg2 : 2.9.5
jinja2 : 3.1.2
IPython : 8.8.0
pandas_datareader: None
bs4 : 4.11.1
bottleneck : 1.3.5
brotli :
fastparquet : 2022.12.0
fsspec : 2022.11.0
gcsfs : 2022.11.0
matplotlib : 3.6.2
numba : 0.56.4
numexpr : 2.8.4
odfpy : None
openpyxl : 3.0.10
pandas_gbq : None
pyarrow : 10.0.1
pyreadstat : 1.2.0
pyxlsb : 1.0.10
s3fs : 2022.11.0
scipy : 1.10.0
snappy :
sqlalchemy : 1.4.46
tables : 3.8.0
tabulate : 0.9.0
xarray : 2022.12.0
xlrd : 2.0.1
zstandard : 0.19.0
tzdata : 2022.7
qtpy : None
pyqt5 : None
None
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