BUG: Concatenating datetime with different resolution fails

[robert-schmidtke]

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
df = pd.DataFrame({"col": [pd.Timestamp.now()]}, dtype="datetime64[ns]")
df2 = pd.DataFrame({"col": [pd.Timestamp.now(), pd.Timestamp.now()]}, dtype="datetime64[us]")
pd.concat([df, df2])

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/rschmidtke/mambaforge/envs/myenv/lib/python3.11/site-packages/pandas/core/reshape/concat.py", line 393, in concat
    return op.get_result()
           ^^^^^^^^^^^^^^^
  File "/home/rschmidtke/mambaforge/envs/myenv/lib/python3.11/site-packages/pandas/core/reshape/concat.py", line 680, in get_result
    new_data = concatenate_managers(
               ^^^^^^^^^^^^^^^^^^^^^
  File "/home/rschmidtke/mambaforge/envs/myenv/lib/python3.11/site-packages/pandas/core/internals/concat.py", line 189, in concatenate_managers
    values = _concatenate_join_units(join_units, copy=copy)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/rschmidtke/mambaforge/envs/myenv/lib/python3.11/site-packages/pandas/core/internals/concat.py", line 486, in _concatenate_join_units
    concat_values = concat_compat(to_concat, axis=1)
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/rschmidtke/mambaforge/envs/myenv/lib/python3.11/site-packages/pandas/core/dtypes/concat.py", line 132, in concat_compat
    return cls._concat_same_type(to_concat_eas)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/rschmidtke/mambaforge/envs/myenv/lib/python3.11/site-packages/pandas/core/arrays/datetimelike.py", line 2246, in _concat_same_type
    new_obj = super()._concat_same_type(to_concat, axis)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/rschmidtke/mambaforge/envs/myenv/lib/python3.11/site-packages/pandas/core/arrays/_mixins.py", line 230, in _concat_same_type
    return super()._concat_same_type(to_concat, axis=axis)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "arrays.pyx", line 190, in pandas._libs.arrays.NDArrayBacked._concat_same_type
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 1 and the array at index 1 has size 2

Issue Description

When concatenating datetime columns with different resolution, e.g. ns and us, an array dimension error is raised.

Expected Behavior

Up until pandas 2.0, this would result in the object dtype on the result

>>> import pandas as pd
>>> df = pd.DataFrame({"col": [pd.Timestamp.now()]}, dtype="datetime64[ns]")
>>> df2 = pd.DataFrame({"col": [pd.Timestamp.now(), pd.Timestamp.now()]}, dtype="datetime64[us]")
>>> pd.concat([df, df2])
                          col
0  2023-09-08 16:49:44.876710
0  2023-09-08 16:49:49.084212
1  2023-09-08 16:49:49.084230
>>> pd.concat([df, df2]).dtypes
col    object
dtype: object

Installed Versions

Details

INSTALLED VERSIONS

commit : ba1cccd
python : 3.11.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.15.90.1-microsoft-standard-WSL2
Version : #1 SMP Fri Jan 27 02:56:13 UTC 2023
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.1.0
numpy : 1.25.2
pytz : 2023.3.post1
dateutil : 2.8.2
setuptools : 68.1.2
pip : 23.2.1
Cython : None
pytest : 7.4.2
hypothesis : None
sphinx : 7.2.5
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.15.0
pandas_datareader : None
bs4 : 4.12.2
bottleneck : None
dataframe-api-compat: None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 13.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None

[paulreece]

I have tested this on 2.1.0 and can confirm it is a bug.

[ccfreeman]

I am seeing a similar issue, but it appears to be more related to Pyarrow 13.0.0. Pandas==2.1.0 and Pyarrow==12.0.0 works fine, while Pandas==2.1.0 and Pyarrow==13.0.0 throws this error.

[hkhojasteh]

@jorisvandenbossche I with investigate the issue and in case it will be related to pandas itself, I will try to fix it :)