BUG: pd.concat produces misleading error messages when datetime types differ since Pandas 2.1.0

[sergiykhan]

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

df1 = pd.DataFrame(
    {
        'date': [pd.Timestamp('2022-04-01'), pd.Timestamp('2022-04-01')],
    }
)

df2 = pd.DataFrame(index=[0, 1])
df2['date'] = pd.Timestamp('2022-04-01')

# Message #1
pd.concat(
    [
        df1,
        df2,
    ],
)

# Message #2
pd.concat(
    [
        df1,
        df2.head(1),
    ],
)

Issue Description

pd.concat() returns misleading error messages

Message 1: ValueError: Shape of passed values is (2, 1), indices imply (4, 1)

Message 2: ValueError: all the input array dimensions except for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 2 and the array at index 1 has size 1

This issue is related to #55879.

Expected Behavior

I am not sure what the expected behaviour should be. Likely a dataframe with the column date of type datetime64[ns].

Installed Versions

INSTALLED VERSIONS

commit : a60ad39
python : 3.11.5.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 : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.1.2
numpy : 1.24.2
pytz : 2023.3.post1
dateutil : 2.8.2
setuptools : 65.5.0
pip : 23.3.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.9.6
jinja2 : None
IPython : 8.16.1
pandas_datareader : None
bs4 : None
bottleneck : None
dataframe-api-compat: None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.8.0
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.4.49
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None

[sergiykhan]

Ok, found it to be a duplicate of #53640, I think. Let me test on the dev branch again.

[sergiykhan]

Ok, the bug appears to be fixed in the main branch of Pandas.