pandas-dev/pandas · error · TypeError

unsupported type

Error message

unsupported type: {into}

What it means

Raised by the to_dict helper (converted_to_mapping) when the `into` argument is not a class or instance of a collections.abc.Mapping subclass. The helper first normalizes instances to their type, then checks issubclass(into, abc.Mapping); any non-Mapping type (list, set, tuple, a custom class without Mapping) triggers TypeError with the offending type rendered.

Solutions

1. Pass a Mapping subclass: dict (default), collections.OrderedDict, collections.defaultdict instance.
2. For list output, use ser.tolist() or df.to_dict('list').
3. If using a custom class, subclass collections.abc.Mapping first.

Example fix

// before
out = ser.to_dict(into=list)
// after
out = ser.tolist()  # or ser.to_dict(into=collections.OrderedDict)

import collections.abc
if not (inspect.isclass(into) and issubclass(into, collections.abc.Mapping)):
    raise TypeError(f'{into} is not a Mapping subclass')
out = ser.to_dict(into=into)

def is_mapping_factory(into) -> bool:
    import collections.abc, inspect
    cls = into if inspect.isclass(into) else type(into)
    return issubclass(cls, collections.abc.Mapping)

When it happens

Trigger: ser.to_dict(into=list); df.to_dict(into=set); ser.to_dict(into=collections.OrderedDict) is fine (Mapping subclass); ser.to_dict(into=tuple) fails.

Common situations: Confusing to_dict 'into' (a mapping factory) with tolist()/records; passing a custom dataclass or namedtuple thinking it acts as a dict; old tutorials suggesting list.

Related errors

• to_dict() only accepts initialized defaultdicts
• Expected Hashable, got
• to_numpy() got an unexpected keyword argument
• Accumulation not supported for
• boolean value of an expression is ambiguous

AI-assisted analysis of pandas-dev/pandas@3b7651241d (2026-08-11). Data as JSON: /api/errors/b1a0be130287939d. Report an issue: GitHub.

Appendix: source

Thrown at pandas/core/common.py:428

        or an instance of a collections.abc.Mapping subclass.

    Returns
    -------
    mapping : a collections.abc.Mapping subclass or other constructor
        a callable object that can accept an iterator to create
        the desired Mapping.

    See Also
    --------
    DataFrame.to_dict
    Series.to_dict
    """
    if not inspect.isclass(into):
        if isinstance(into, defaultdict):
            return partial(defaultdict, into.default_factory)
        into = type(into)
    if not issubclass(into, abc.Mapping):
        raise TypeError(f"unsupported type: {into}")
    if into == defaultdict:
        raise TypeError("to_dict() only accepts initialized defaultdicts")
    return into


@overload
def random_state(state: np.random.Generator) -> np.random.Generator: ...


@overload
def random_state(
    state: int | np.ndarray | np.random.BitGenerator | np.random.RandomState | None,
) -> np.random.RandomState: ...


def random_state(
    state: RandomState | None = None,
) -> np.random.RandomState | np.random.Generator:

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