BUG: aggregate function on transposed resampled data frame raises ValueError

[provinzio]

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Reproducible Example

import pandas as pd
DIM = 3
now = pd.Timestamp.now()
# Create data frame.
data = [[i + j for i in range(DIM)] for j in range(DIM)]
columns = [now + pd.DateOffset(i) for i in range(DIM)]
df = pd.DataFrame(data=data, columns=columns)
# Calculate mean/min/max/sum per month.
df.resample("M", axis=1).aggregate(["mean", "min", "max", "sum"])

Issue Description

I am trying to calculate the mean/min/max/sum per month of a dataframe containing timestamps as column indexes. Running the above MWE raises the following error.

Traceback (most recent call last):
  File "d:\current\29 XV DATEV Auswertungen\venv\lib\site-packages\pandas\core\generic.py", line 550, in _get_axis_number
    return cls._AXIS_TO_AXIS_NUMBER[axis]
KeyError: 1

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "d:\current\29 XV DATEV Auswertungen\venv\lib\site-packages\pandas\core\resample.py", line 347, in aggregate
    result = ResamplerWindowApply(self, func, args=args, kwargs=kwargs).agg()
  File "d:\current\29 XV DATEV Auswertungen\venv\lib\site-packages\pandas\core\apply.py", line 171, in agg
    return self.agg_list_like()
  File "d:\current\29 XV DATEV Auswertungen\venv\lib\site-packages\pandas\core\apply.py", line 367, in agg_list_like
    colg = obj._gotitem(col, ndim=1, subset=selected_obj.iloc[:, index])
  File "d:\current\29 XV DATEV Auswertungen\venv\lib\site-packages\pandas\core\resample.py", line 412, in _gotitem
    grouped = get_groupby(subset, by=None, grouper=grouper, axis=self.axis)
  File "d:\current\29 XV DATEV Auswertungen\venv\lib\site-packages\pandas\core\groupby\groupby.py", line 3846, in get_groupby
    return klass(
  File "d:\current\29 XV DATEV Auswertungen\venv\lib\site-packages\pandas\core\groupby\groupby.py", line 894, in __init__
    self.axis = obj._get_axis_number(axis)
  File "d:\current\29 XV DATEV Auswertungen\venv\lib\site-packages\pandas\core\generic.py", line 552, in _get_axis_number
    raise ValueError(f"No axis named {axis} for object type {cls.__name__}")
ValueError: No axis named 1 for object type Series

Expected Behavior

I'd like to create a dataframe where the mean/min/max/sum per month is calculated. Following to the documentation, I'd expect the code to run without errors.

Installed Versions

INSTALLED VERSIONS

commit : 4bfe3d0
python : 3.9.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : de_DE.UTF-8
LOCALE : de_DE.cp1252

pandas : 1.4.2
numpy : 1.22.0
pytz : 2021.3
dateutil : 2.8.2
pip : 22.0.4
setuptools : 49.2.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 3.0.2
lxml.etree : 4.8.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.3
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
markupsafe : 2.0.1
matplotlib : 3.5.1
numba : None
numexpr : None
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : None
snappy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
zstandard : None

[provinzio]

Transposing the DataFrame (having the timestamps in axis=0) works, but adds (for my case) the aggregated multi-indexed columns on the wrong dimension.
df.T.resample("M").aggregate(["mean", "min", "max", "sum"])

Edit:
The following line does what I want without errors
df.T.resample("M").aggregate(["mean", "min", "max", "sum"]).stack().T

[simonjayhawkins]

Thanks @provinzio for the report.

In 1.2.5, the code sample raises a more helpful NotImplementedError: axis other than 0 is not supported. Will label as regression for now pending further investigation.

first bad commit: [8c62fbb] CLN: Remove _axis from apply (#40261)

cc @rhshadrach

[rhshadrach]

Thanks @provinzio; agreed with @simonjayhawkins, this should raise a better error message.