BUG: rolling with axis=1, win_type=, and center=True raises ValueError

[mvashishtha]

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([[0]])
# this works:
print(df.rolling(window=1, axis=0, win_type="boxcar", center=True).sum())
# This raises ValueError:
print(df.rolling(window=1, axis=1, win_type="boxcar", center=True).sum())

Issue Description

I start with a dataframe with a single value, 0. Rolling over columns with the above parameters gives:

     0
0  3.0

Rolling over rows with axis=1 instead gives ValueError: Requested axis is larger then no. of argument dimensions:

Show stack trace

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Input In [48], in <module>
----> 1 df.rolling(window=1, axis=1, win_type="boxcar", center=True).sum()

File /usr/local/lib/python3.9/site-packages/pandas/core/window/rolling.py:1216, in Window.sum(self, *args, **kwargs)
   1212 window_func = window_aggregations.roll_weighted_sum
   1213 # error: Argument 1 to "_apply" of "Window" has incompatible type
   1214 # "Callable[[ndarray, ndarray, int], ndarray]"; expected
   1215 # "Callable[[ndarray, int, int], ndarray]"
-> 1216 return self._apply(window_func, name="sum", **kwargs)

File /usr/local/lib/python3.9/site-packages/pandas/core/window/rolling.py:1154, in Window._apply(self, func, name, numba_cache_key, numba_args, **kwargs)
   1150         result = self._center_window(result, offset)
   1152     return result
-> 1154 return self._apply_blockwise(homogeneous_func, name)

File /usr/local/lib/python3.9/site-packages/pandas/core/window/rolling.py:462, in BaseWindow._apply_blockwise(self, homogeneous_func, name)
    459 for i, arr in enumerate(obj._iter_column_arrays()):
    460     # GH#42736 operate column-wise instead of block-wise
    461     try:
--> 462         res = hfunc(arr)
    463     except (TypeError, NotImplementedError):
    464         pass

File /usr/local/lib/python3.9/site-packages/pandas/core/window/rolling.py:452, in BaseWindow._apply_blockwise.<locals>.hfunc(values)
    450 def hfunc(values: ArrayLike) -> ArrayLike:
    451     values = self._prep_values(values)
--> 452     return homogeneous_func(values)

File /usr/local/lib/python3.9/site-packages/pandas/core/window/rolling.py:1150, in Window._apply.<locals>.homogeneous_func(values)
   1147     result = np.asarray(calc(values))
   1149 if self.center:
-> 1150     result = self._center_window(result, offset)
   1152 return result

File /usr/local/lib/python3.9/site-packages/pandas/core/window/rolling.py:1092, in Window._center_window(self, result, offset)
   1088 """
   1089 Center the result in the window for weighted rolling aggregations.
   1090 """
   1091 if self.axis > result.ndim - 1:
-> 1092     raise ValueError("Requested axis is larger then no. of argument dimensions")
   1094 if offset > 0:
   1095     lead_indexer = [slice(None)] * result.ndim

ValueError: Requested axis is larger then no. of argument dimensions

Expected Behavior

I expect rolling over axis 1 to give the same result as rolling over axis 0 for this single-element dataframe.

Installed Versions

INSTALLED VERSIONS

commit : b4e578d
python : 3.9.10.final.0
python-bits : 64
OS : Darwin
OS-release : 21.3.0
Version : Darwin Kernel Version 21.3.0: Wed Jan 5 21:37:58 PST 2022; root:xnu-8019.80.24~20/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.5.0.dev0+122.gb4e578db85
numpy : 1.22.1
pytz : 2021.3
dateutil : 2.8.2
pip : 22.0.3
setuptools : 60.8.2
Cython : 0.29.27
pytest : 7.0.0
hypothesis : None
sphinx : 4.4.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.9.3
jinja2 : 3.0.3
IPython : 8.0.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
fsspec : 2022.01.0
gcsfs : None
matplotlib : 3.5.1
numba : None
numexpr : 2.8.1
odfpy : None
openpyxl : 3.0.9
pandas_gbq : 0.16.0
pyarrow : 6.0.1
pyreadstat : None
pyxlsb : None
s3fs : 2022.01.0
scipy : 1.7.3
sqlalchemy : 1.4.31
tables : 3.7.0
tabulate : None
xarray : 0.20.2
xlrd : 2.0.1
xlwt : None
zstandard : None