BUG: pd.concat ignores axis="columns" argument

[CarlaFernandez]

Pandas version checks

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• 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.read_json('{"PassengerId":{"1":2.0},"Survived":{"1":1.0},"Pclass":{"1":1.0},"Name":{"1":"Cumings, Mrs. John Bradley (Florence Briggs Thayer)"},"Sex":{"1":"female"},"Age":{"1":38.0},"SibSp":{"1":1.0},"Parch":{"1":0.0},"Ticket":{"1":"PC 17599"},"Fare":{"1":71.2833},"Cabin":{"1":"C85"},"Embarked":{"1":"C"},"score":{"1":1.0}}')
df2 = pd.read_json('{"score_calib":{"0":0.9647065937}}')
pd.concat((df1, df2), axis="columns")

Issue Description

The result from the reproducible example is the following:

This seems to be due to the differences in the indices (df1 has index 1 and df2 has index 0), however I think pd.concat should either:

1. Ignore this and concat the dataframes as-is, raising a warning and creating a new index for the resulting dataframe.
2. Raise an exception that the indexes are different and break.

Because of the nature and usage of pd.concat(axis="columns"), I think alternative 1 would be preferable.

Expected Behavior

The expected behaviour is to obtain a dataframe with a single row and 14 columns like this one:

Installed Versions

On a Databricks instance:

INSTALLED VERSIONS

commit : 2cb9652
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-1090-azure
Version : #95~18.04.1-Ubuntu SMP Sun Aug 14 20:09:27 UTC 2022
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.2.4
numpy : 1.22.4
pytz : 2020.5
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0
Cython : 0.29.23
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 8.4.0
pandas_datareader: None
bs4 : 4.11.1
bottleneck : 1.3.2
fsspec : 0.9.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : 0.55.2

Also happening in a Windows environment:

INSTALLED VERSIONS

commit : e8093ba
python : 3.10.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19044
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 11, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : es_ES.cp1252
pandas : 1.4.3
numpy : 1.23.1
pytz : 2022.1
dateutil : 2.8.2
setuptools : 60.2.0
pip : 21.3.1
Cython : None
pytest : 7.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
markupsafe : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.9.0
snappy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
zstandard : None

[CarlaFernandez]

Closing the issue, just realized this is what the argument ignore_index=True is for