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Reproducible Example

import pandas as pd

nodes=pd.CategoricalIndex(list('abcde'), name='nodes')
edges=pd.CategoricalIndex([0,1,2,3], name='edges')
flags=pd.MultiIndex.from_product((edges,nodes))

levi= pd.Series(1, index=flags, dtype='Sparse[int]').sample(10)
levi.sparse.to_coo(row_levels=['edges'], column_levels=['nodes'])

Issue Description

example levi: 

nodes  edges
d      2        1
a      1        1
c      1        1
d      0        1
c      2        1
       0        1
e      1        1
b      1        1
c      3        1
b      3        1
dtype: Sparse[int64, 0]

Error stacktrace: 

---------------------------------------------------------------------------
SystemError                               Traceback (most recent call last)
Cell In[44], line 1
----> 1 levi.sparse.to_coo(row_levels=['edges'], column_levels=['nodes'])

File ~/.pyenv/versions/grabble/lib/python3.10/site-packages/pandas/core/arrays/sparse/accessor.py:187, in SparseAccessor.to_coo(self, row_levels, column_levels, sort_labels)
    111 """
    112 Create a scipy.sparse.coo_matrix from a Series with MultiIndex.
    113 
   (...)
    183 [('a', 0), ('a', 1), ('b', 0), ('b', 1)]
    184 """
    185 from pandas.core.arrays.sparse.scipy_sparse import sparse_series_to_coo
--> 187 A, rows, columns = sparse_series_to_coo(
    188     self._parent, row_levels, column_levels, sort_labels=sort_labels
    189 )
    190 return A, rows, columns

File ~/.pyenv/versions/grabble/lib/python3.10/site-packages/pandas/core/arrays/sparse/scipy_sparse.py:167, in sparse_series_to_coo(ss, row_levels, column_levels, sort_labels)
    164 row_levels = [ss.index._get_level_number(x) for x in row_levels]
    165 column_levels = [ss.index._get_level_number(x) for x in column_levels]
--> 167 v, i, j, rows, columns = _to_ijv(
    168     ss, row_levels=row_levels, column_levels=column_levels, sort_labels=sort_labels
    169 )
    170 sparse_matrix = scipy.sparse.coo_matrix(
    171     (v, (i, j)), shape=(len(rows), len(columns))
    172 )
    173 return sparse_matrix, rows, columns

File ~/.pyenv/versions/grabble/lib/python3.10/site-packages/pandas/core/arrays/sparse/scipy_sparse.py:132, in _to_ijv(ss, row_levels, column_levels, sort_labels)
    129 values = sp_vals[na_mask]
    130 valid_ilocs = ss.array.sp_index.indices[na_mask]
--> 132 i_coords, i_labels = _levels_to_axis(
    133     ss, row_levels, valid_ilocs, sort_labels=sort_labels
    134 )
    136 j_coords, j_labels = _levels_to_axis(
    137     ss, column_levels, valid_ilocs, sort_labels=sort_labels
    138 )
    140 return values, i_coords, j_coords, i_labels, j_labels

File ~/.pyenv/versions/grabble/lib/python3.10/site-packages/pandas/core/arrays/sparse/scipy_sparse.py:75, in _levels_to_axis(ss, levels, valid_ilocs, sort_labels)
     72     ax_labels = ss.index.levels[levels[0]]
     74 else:
---> 75     levels_values = lib.fast_zip(
     76         [ss.index.get_level_values(lvl).values for lvl in levels]
     77     )
     78     codes, ax_labels = factorize(levels_values, sort=sort_labels)
     79     ax_coords = codes[valid_ilocs]

File ~/.pyenv/versions/grabble/lib/python3.10/site-packages/pandas/_libs/lib.pyx:475, in pandas._libs.lib.fast_zip()

SystemError: numpy/core/src/multiarray/iterators.c:185: bad argument to internal function

Expected Behavior

I expect to get back an incidence matrix with the correct number of entries (e.g. 10), with shape based on the categorical dtype... shape=(|edges.categories|,|nodes.categories|), or optionally the shape could be from only the observed edges(shape=(|edges|,|nodes|),

Installed Versions

INSTALLED VERSIONS ------------------ commit : 8dab54d6573f7186ff0c3b6364d5e4dd635ff3e7 python : 3.10.8.final.0 python-bits : 64 OS : Linux OS-release : 5.4.0-135-generic Version : #152-Ubuntu SMP Wed Nov 23 20:19:22 UTC 2022 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.5.2 numpy : 1.23.5 pytz : 2022.6 dateutil : 2.8.2 setuptools : 65.6.3 pip : 22.3.1 Cython : None pytest : 5.4.3 hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : 1.1 pymysql : None psycopg2 : None jinja2 : 3.1.2 IPython : 8.7.0 pandas_datareader: None bs4 : 4.11.1 bottleneck : None brotli : 1.0.9 fastparquet : None fsspec : None gcsfs : None matplotlib : 3.6.2 numba : 0.56.4 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyreadstat : None pyxlsb : None s3fs : None scipy : 1.9.3 snappy : None sqlalchemy : None tables : None tabulate : None xarray : 2022.11.0 xlrd : None xlwt : None zstandard : None tzdata : None