code: def _sparse_series_to_coo(ss, row_levels=(0, ), column_levels=(1, ),
                            sort_labels=False):
      
  
      import scipy.sparse
  
      if ss.index.nlevels < 2:
          raise ValueError('to_coo requires MultiIndex with nlevels > 2')
      if not ss.index.is_unique:
          raise ValueError('Duplicate index entries are not allowed in to_coo '
                           'transformation.')
  
      # to keep things simple, only rely on integer indexing (not labels)
      row_levels = [ss.index._get_level_number(x) for x in row_levels]
      column_levels = [ss.index._get_level_number(x) for x in column_levels]
  
      v, i, j, rows, columns = _to_ijv(ss, row_levels=row_levels,
                                       column_levels=column_levels,
                                       sort_labels=sort_labels)
      sparse_matrix = scipy.sparse.coo_matrix(
          (v, (i, j)), shape=(len(rows), len(columns)))
      return sparse_matrix, rows, columns
docs: Convert a SparseSeries to a scipy.sparse.coo_matrix using index
      levels row_levels, column_levels as the row and column
      labels respectively. Returns the sparse_matrix, row and column labels.