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Reproducible Example

# ISO:
In [1]: pd.to_datetime(["2020-01-01 01:00:00-01:00", datetime(2020, 1, 1, 3, 0)], format='%Y-%m-%d %H:%M:%S%z')
Out[1]: DatetimeIndex(['2020-01-01 01:00:00-01:00', '2020-01-01 02:00:00-01:00'], dtype='datetime64[ns, UTC-01:00]', freq=None)

# non-ISO:
In [2]: pd.to_datetime(["2020-01-01 01:00:00-01:00", datetime(2020, 1, 1, 3, 0)], format='%Y-%d-%m %H:%M:%S%z')
Out[2]: Index([2020-01-01 01:00:00-01:00, 2020-01-01 03:00:00], dtype='object')

Issue Description

This is inconsistent. I think I prefer the latter - datetime(2020, 1, 1, 3, 0) is timezone-naive, so it seems right to do the same thing we'd do if there were mixed offsets, i.e. return an Index of dtype object

As far as I can tell, the only place this is tested is

https://github.com/pandas-dev/pandas/blob/1d5f05c33c613508727ee7b971ad56723d474446/pandas/core/tools/datetimes.py#L993-L1000

Timezone-naive and timezone-aware are not the same format, so if we now expect elements to be parsed with the same format, I don't think this should fly

Expected Behavior

Same output in both cases, I think this should be:

In [1]: pd.to_datetime(["2020-01-01 01:00:00-01:00", datetime(2020, 1, 1, 3, 0)], format='%Y-%m-%d %H:%M:%S%z')  # ISO: naive dt.datetime is converted
Out[1]: Index([2020-01-01 01:00:00-01:00, 2020-01-01 03:00:00], dtype='object')

In [2]: pd.to_datetime(["2020-01-01 01:00:00-01:00", datetime(2020, 1, 1, 3, 0)], format='%Y-%d-%m %H:%M:%S%z')  # non-ISO: dt.datetime is left as-is, object Index returned
Out[2]: Index([2020-01-01 01:00:00-01:00, 2020-01-01 03:00:00], dtype='object')

Installed Versions

INSTALLED VERSIONS ------------------ commit : 113bdb37694e2849a15c390fddd8f28986bf1863 python : 3.8.15.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+918.g113bdb3769 numpy : 1.23.5 pytz : 2022.6 dateutil : 2.8.2 setuptools : 65.5.1 pip : 22.3.1 Cython : 0.29.32 pytest : 7.2.0 hypothesis : 6.61.0 sphinx : 4.5.0 blosc : None feather : None xlsxwriter : 3.0.3 lxml.etree : 4.9.1 html5lib : 1.1 pymysql : 1.0.2 psycopg2 : 2.9.3 jinja2 : 3.1.2 IPython : 8.7.0 pandas_datareader: None bs4 : 4.11.1 bottleneck : 1.3.5 brotli : fastparquet : 2022.12.0 fsspec : 2021.11.0 gcsfs : 2021.11.0 matplotlib : 3.6.2 numba : 0.56.4 numexpr : 2.8.3 odfpy : None openpyxl : 3.0.10 pandas_gbq : None pyarrow : 9.0.0 pyreadstat : 1.2.0 pyxlsb : 1.0.10 s3fs : 2021.11.0 scipy : 1.9.3 snappy : sqlalchemy : 1.4.45 tables : 3.7.0 tabulate : 0.9.0 xarray : 2022.12.0 xlrd : 2.0.1 zstandard : 0.19.0 tzdata : None qtpy : None pyqt5 : None None

Comment From: jorisvandenbossche

Resolved by https://github.com/pandas-dev/pandas/pull/50242