pandas-dev/pandas · error · TypeError

unsupported type: {into}

Error message

unsupported type: {into}

What it means

Raised by standardize_mapping (pandas/core/common.py:428) used by Series.to_dict / DataFrame.to_dict when the `into` argument is not a subclass of collections.abc.Mapping. to_dict needs a dict-like constructor to build the result, so plain types like list, set, tuple, or arbitrary classes are rejected.

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:

View on GitHub (pinned to 71959b8cb9)

Solutions

1. Pass a Mapping subclass: `into=dict` (default), `into=collections.OrderedDict`, or `into=collections.defaultdict(list)` (an instance).
2. If you need a non-dict shape, call to_dict with the right orient and convert afterward, e.g. `df.to_dict('records')` returns a list of dicts.
3. For a custom Mapping, ensure it subclasses collections.abc.Mapping and implements __getitem__, __iter__, __len__.

Example fix

# before
df.to_dict(into=list)

# after
list(df.to_dict('index').items())

import collections.abc

def validate_into(into):
    cls = into if isinstance(into, type) else type(into)
    if not issubclass(cls, collections.abc.Mapping):
        raise TypeError(f'into must be a Mapping subclass, got {cls}')
    return into

import collections.abc

def is_mapping_type(into) -> bool:
    cls = into if isinstance(into, type) else type(into)
    return issubclass(cls, collections.abc.Mapping)

try:
    result = df.to_dict(into=into)
except TypeError as e:
    if 'unsupported type' in str(e):
        result = df.to_dict(into=dict)
    else:
        raise

When it happens

Trigger: `df.to_dict('records', into=list)`, `s.to_dict(into=set)`, `df.to_dict(into=tuple)`, or `into=SomeCustomClass` that does not subclass Mapping.

Common situations: Misunderstanding `into` as the output container type rather than a Mapping subclass. Passing a custom class that forgot to inherit from abc.Mapping.

Related errors

• to_dict() only accepts initialized defaultdicts
• Expected Hashable, got: {type(col_name)}
• Resolver of type '{name}' does not implement the __getitem__
• No such keys(s): {pat!r}
• {k} is not a valid identifier

AI-assisted analysis of pandas-dev/pandas@71959b8cb9 (2026-08-07). Data as JSON: /api/errors/b1a0be130287939d. Report an issue: GitHub.