pola-rs/polars · error · TypeError

cannot select elements using key of type {qualified_type_nam

Error message

cannot select elements using key of type {qualified_type_name(key)!r}: {key!r}

What it means

This is the terminal fallback of Series.__getitem__: the key matched none of the supported types (int, slice, Sequence, pl.Series, NumPy ndarray). Keys like a bare float, dict, or arbitrary object reach this branch and raise TypeError with the key's type and repr.

Source

Thrown at py-polars/src/polars/_utils/getitem.py:90

        try:
            indices = pl.Series("", key, dtype=Int64)
        except TypeError:
            msg = f"cannot select elements using Sequence with elements of type {qualified_type_name(first)!r}"
            raise TypeError(msg) from None

        indices = _convert_series_to_indices(indices, s.len())
        return _select_elements_by_index(s, indices)

    elif isinstance(key, pl.Series):
        indices = _convert_series_to_indices(key, s.len())
        return _select_elements_by_index(s, indices)

    elif _check_for_numpy(key) and isinstance(key, np.ndarray):
        indices = _convert_np_ndarray_to_indices(key, s.len())
        return _select_elements_by_index(s, indices)

    msg = f"cannot select elements using key of type {qualified_type_name(key)!r}: {key!r}"
    raise TypeError(msg)


def _select_elements_by_slice(s: Series, key: slice) -> Series:
    return PolarsSlice(s).apply(key)  # type: ignore[return-value]


def _select_elements_by_index(s: Series, key: Series) -> Series:
    return s._from_pyseries(s._s.gather_with_series(key._s))


# `str` overlaps with `Sequence[str]`
# We can ignore this but we must keep this overload ordering
@overload
def get_df_item_by_key(
    df: DataFrame, key: tuple[SingleIndexSelector, SingleColSelector]
) -> Any: ...

View on GitHub (pinned to df599052da)

Solutions

1. Convert integral floats: s[int(key)].
2. Wrap multiple values into a list, pl.Series, or np.array before indexing.
3. For Boolean selection pass a list/Series of bools, not a dict or object.

Example fix

// before
i = positions.mean()  # float
val = s[i]

// after
val = s[int(positions.mean())]

def normalize_series_key(key):
    if isinstance(key, float) and key.is_integer():
        return int(key)
    if isinstance(key, (list, tuple, pl.Series)) or isinstance(key, slice) or isinstance(key, int):
        return key
    raise TypeError(f"unsupported Series key type: {type(key).__name__}")

val = s[normalize_series_key(key)]

def is_supported_series_key(key: object) -> bool:
    return isinstance(key, (int, slice, list, tuple, pl.Series)) or (
        _check_numpy(key) and isinstance(key, __import__("numpy").ndarray)
    )

try:
    val = s[key]
except TypeError as e:
    if "cannot select elements using key of type" in str(e):
        raise TypeError(f"bad index {key!r}; pass int, slice, list[int], Series, or ndarray") from e
    raise

When it happens

Trigger: s[1.0]; s[{"a": 1}]; s[some_custom_object]; a numpy scalar that is neither int-subclass nor ndarray slipping through.

Common situations: Dict keys or JSON numbers flowing into indexing code; floats from computed indices not rounded to int; passing a mapping where a key or list was intended.

Related errors

• cannot select elements using Sequence with elements of type
• cannot use Series of dtype {key.dtype!r} for indexing; expec
• cannot use "{key!r}" for indexing
• unsupported type {qualified_type_name(arg)!r} for {arg!r}
• passing Expr objects to the DataFrame constructor is not sup

AI-assisted analysis of pola-rs/polars@df599052da (2026-08-16). Data as JSON: /api/errors/eb63ed325abdf609. Report an issue: GitHub.