BUG: pd.concat() cannot concatenate more than 3 mixed-frequency dataframes with multi-index series

[sakaiando]

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

n = 5
p = 2
dfA = pd.DataFrame(np.random.rand(n,p),
                   columns=['A0','B0'],
                   index=pd.period_range(start='2000-1-1',periods=n,freq='Y'))

dfQ = pd.DataFrame(np.random.rand(n,p),
                   columns=['A0','B0'],
                   index=pd.period_range(start='2000-1-1',periods=n,freq='Q'))

dfM = pd.DataFrame(np.random.rand(n,p),
                   columns=['A0','B0'],
                   index=pd.period_range(start='2000-1-1',periods=n,freq='M'))

df1 = dfA.loc[:,['A0']].T.stack()
df2 = dfA.loc[:,['B0']].T.stack()
df3 = dfQ.loc[:,['A0']].T.stack()
df4 = dfQ.loc[:,['B0']].T.stack()
df5 = dfM.loc[:,['A0']].T.stack()
df6 = dfM.loc[:,['B0']].T.stack()

# these work
pd.concat([df1,df4])
pd.concat([df1,df2])
pd.concat([df1,df3])
pd.concat([df1,df2,df3])
pd.concat([dfA['A0'],dfQ['A0'],dfM['A0']])

# error
pd.concat([df1,df3,df2])
pd.concat([df1,df2,df3,df4])
pd.concat([df1,df3,df5])

Issue Description

pd.concat() can concatenate 3 multi-index dataframes when they have 2 frequencies, but cannot concatenate 4 dataframes.

Expected Behavior

pd.concat() should be able to concatenate a list of mixed-freq multiindex dataframes no matter how many items the list has.

Installed Versions

INSTALLED VERSIONS

commit : d9cdd2e
python : 3.12.4.final.0
python-bits : 64
OS : Windows
OS-release : 2016Server
Version : 10.0.14393
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : English_United States.1252

pandas : 2.2.2
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0.post0
setuptools : 72.1.0
pip : 24.0
Cython : 3.0.11
pytest : None
hypothesis : None
sphinx : 8.0.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.4
IPython : 8.26.0
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2024.6.1
gcsfs : None
matplotlib : 3.9.1.post1
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 17.0.0
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.14.0
sqlalchemy : None
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
zstandard : None
tzdata : 2024.1
qtpy : 2.4.1
pyqt5 : None

[rhshadrach]

Thanks for the report, agreed this looks inconsistent. I'm guessing we should raise rather than converting to object dtype. @jbrockmendel - any thoughts?