@classmethod
def from_coo(cls, A, dense_index=False):
    """
        Create a Series with sparse values from a scipy.sparse.coo_matrix.

        Parameters
        ----------
        A : scipy.sparse.coo_matrix
        dense_index : bool, default False
            If False (default), the SparseSeries index consists of only the
            coords of the non-null entries of the original coo_matrix.
            If True, the SparseSeries index consists of the full sorted
            (row, col) coordinates of the coo_matrix.

        Returns
        -------
        s : Series
            A Series with sparse values.

        Examples
        --------
        >>> from scipy import sparse
        >>> A = sparse.coo_matrix(([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])),
                               shape=(3, 4))
        >>> A
        <3x4 sparse matrix of type '<class 'numpy.float64'>'
                with 3 stored elements in COOrdinate format>
        >>> A.todense()
        matrix([[ 0.,  0.,  1.,  2.],
                [ 3.,  0.,  0.,  0.],
                [ 0.,  0.,  0.,  0.]])
        >>> ss = pd.Series.sparse.from_coo(A)
        >>> ss
        0  2    1
           3    2
        1  0    3
        dtype: float64
        BlockIndex
        Block locations: array([0], dtype=int32)
        Block lengths: array([3], dtype=int32)
        """
    from pandas.core.arrays.sparse.scipy_sparse import _coo_to_sparse_series
    from pandas import Series
    result = _coo_to_sparse_series(A, dense_index=dense_index)
    result = Series(result.array, index=result.index, copy=False)
    return result
