Function equivalent

xarray/core/utils.py:238–273

Compare two objects for equivalence (identity or equality), using array_equiv if either object is an ndarray. If both objects are lists, equivalent is sequentially called on all the elements. Returns False for any comparison that doesn't return a boolean, making this function safer

(first: T, second: T)

def equivalent(first: T, second: T) -> bool:
    """Compare two objects for equivalence (identity or equality), using
    array_equiv if either object is an ndarray. If both objects are lists,
    equivalent is sequentially called on all the elements.

    Returns False for any comparison that doesn't return a boolean,
    making this function safer to use with objects that have non-standard
    __eq__ implementations.
    """
    # TODO: refactor to avoid circular import
    from xarray.core import duck_array_ops

    if first is second:
        return True

    if isinstance(first, np.ndarray) or isinstance(second, np.ndarray):
        return duck_array_ops.array_equiv(first, second)

    if isinstance(first, list) or isinstance(second, list):
        return list_equiv(first, second)  # type: ignore[arg-type]

    # Check for NaN equivalence early (before equality comparison)
    # This handles both Python float NaN and NumPy scalar NaN (issue #10833)
    if pd.isnull(first) and pd.isnull(second):  # type: ignore[call-overload]
        return True

    # For non-array/list types, use == but require boolean result
    result = first == second
    if not isinstance(result, bool):
        # Accept numpy bool scalars as well
        if isinstance(result, np.bool_):
            return bool(result)
        # Reject any other non-boolean type (Dataset, Series, custom objects, etc.)
        return False

    return result


def list_equiv(first: Sequence[T], second: Sequence[T]) -> bool: