pandas-dev/pandas · error · TypeError

Expected coo_matrix. Got {type(A).__name__} instead.

Error message

Expected coo_matrix. Got {type(A).__name__} instead.

What it means

Raised by coo_to_sparse_series when the input lacks the .data/.row/.col attributes of a scipy.sparse.coo_matrix. The function wraps the attribute access in a try/except AttributeError and re-raises as TypeError so callers get a clear contract violation instead of a confusing AttributeError. Only scipy.sparse.coo_matrix is accepted because the conversion logic reads A.data, A.row, and A.col directly.

Source

Thrown at pandas/core/arrays/sparse/scipy_sparse.py:200

    Parameters
    ----------
    A : scipy.sparse.coo_matrix
    dense_index : bool, default False

    Returns
    -------
    Series

    Raises
    ------
    TypeError if A is not a coo_matrix
    """
    from pandas import SparseDtype

    try:
        ser = Series(A.data, MultiIndex.from_arrays((A.row, A.col)), copy=False)
    except AttributeError as err:
        raise TypeError(
            f"Expected coo_matrix. Got {type(A).__name__} instead."
        ) from err
    ser = ser.sort_index()
    ser = ser.astype(SparseDtype(ser.dtype))
    if dense_index:
        ind = MultiIndex.from_product([A.row, A.col])
        ser = ser.reindex(ind)
    return ser

View on GitHub (pinned to 71959b8cb9)

Solutions

1. Convert the matrix to coo format before calling: coo_to_sparse_series(A.tocoo()).
2. Check the format up front: if A.format != 'coo': A = A.tocoo().
3. Use scipy.sparse.coo_matrix directly when constructing data destined for pandas sparse Series.

Example fix

// before
from pandas.core.arrays.sparse.scipy_sparse import coo_to_sparse_series
series = coo_to_sparse_series(csr_mat)

// after
series = coo_to_sparse_series(csr_mat.tocoo())

import scipy.sparse

def to_sparse_series(A):
    if not (scipy.sparse.issparse(A) and A.format == 'coo'):
        A = A.tocoo()
    return coo_to_sparse_series(A)

import scipy.sparse

def is_coo_matrix(A) -> bool:
    return scipy.sparse.issparse(A) and getattr(A, 'format', None) == 'coo'

When it happens

Trigger: Calling pandas.core.arrays.sparse.scipy_sparse.coo_to_sparse_series with a csr_matrix, csc_matrix, lil_matrix, dok_matrix, a dense numpy.ndarray, a list, or any object that is not a scipy.sparse.coo_matrix. Also triggered indirectly through SparseDtype round-trips that pass the wrong sparse format.

Common situations: User obtains a csr_matrix from scikit-learn or scipy and tries to convert it to a pandas SparseSeries without converting format first. Copy-pasting code that worked on coo output but now feeds in a different sparse format.

Related errors

• Column length mismatch: {len(columns)} vs. {K}
• Index length mismatch: {len(index)} vs. {N}
• Cannot construct {type(self).__name__} from scalar data. Pas
• 'data' must have a single column, not '{ncol}'
• {func_name} requires a Series, Index, ExtensionArray, np.nda

AI-assisted analysis of pandas-dev/pandas@71959b8cb9 (2026-08-07). Data as JSON: /api/errors/15cd2fb01d183d4d. Report an issue: GitHub.