pandas-dev/pandas · error · ValueError

Is not a partition because union is not the whole.

Error message

Is not a partition because union is not the whole.

What it means

Thrown by _check_is_partition in pandas/core/arrays/sparse/scipy_sparse.py:37, the companion check to the intersection error. Here the union of row_levels and column_levels does not cover every level of the MultiIndex — at least one level is omitted from both groups. COO conversion needs to place every index level on exactly one axis, so an uncovered level is rejected.

Source

Thrown at pandas/core/arrays/sparse/scipy_sparse.py:37

if TYPE_CHECKING:
    from collections.abc import Iterable

    import numpy as np
    import scipy.sparse

    from pandas._typing import (
        IndexLabel,
        npt,
    )


def _check_is_partition(parts: Iterable, whole: Iterable) -> None:
    whole = set(whole)
    parts = [set(x) for x in parts]
    if set.intersection(*parts) != set():
        raise ValueError("Is not a partition because intersection is not null.")
    if set.union(*parts) != whole:
        raise ValueError("Is not a partition because union is not the whole.")


def _levels_to_axis(
    ss,
    levels: tuple[int] | list[int],
    valid_ilocs: npt.NDArray[np.intp],
    sort_labels: bool = False,
) -> tuple[npt.NDArray[np.intp], list[IndexLabel]]:
    """
    For a MultiIndexed sparse Series `ss`, return `ax_coords` and `ax_labels`,
    where `ax_coords` are the coordinates along one of the two axes of the
    destination sparse matrix, and `ax_labels` are the labels from `ss`' Index
    which correspond to these coordinates.

    Parameters
    ----------
    ss : Series
    levels : tuple/list

View on GitHub (pinned to 3b7651241d)

Solutions

1. List every level across the two groups: row_levels=[0], column_levels=[1,2] for a 3-level index.
2. Compute levels programmatically: row_levels=[0]; column_levels=[l for l in range(ss.index.nlevels) if l not in row_levels].
3. Verify coverage: assert set(row_levels) | set(column_levels) == set(range(ss.index.nlevels)).

Example fix

// before
# 3-level MultiIndex
ss.sparse.to_coo(row_levels=[0], column_levels=[1])  # level 2 uncovered

// after
ss.sparse.to_coo(row_levels=[0], column_levels=[1, 2])

def covering_levels(row_levels, column_levels, nlevels):
    missing = set(range(nlevels)) - (set(row_levels) | set(column_levels))
    if missing:
        raise ValueError(f'levels {missing} not assigned to any axis')
    return row_levels, column_levels

null

null

When it happens

Trigger: Calling ss.sparse.to_coo(row_levels=[0], column_levels=[1]) on a Series whose MultiIndex has 3 levels — level 2 is in neither group. Forgetting to list a level after extending the MultiIndex.

Common situations: Default row_levels=[0], column_levels=[1] used on a 3-level index without updating the call. Refactoring code that added a level to the MultiIndex but did not update to_coo arguments.

Related errors

• Is not a partition because intersection is not null.
• to_coo requires MultiIndex with nlevels >= 2.
• Duplicate index entries are not allowed in to_coo transforma
• Expected coo_matrix. Got {type(A).__name__} instead.
• Column length mismatch: {len(columns)} vs. {K}

AI-assisted analysis of pandas-dev/pandas@3b7651241d (2026-08-11). Data as JSON: /api/errors/cedcb108aa18d9a4. Report an issue: GitHub.