pandas-dev/pandas · error · TypeError

{cls.__name__}(...) must be called with a collection of some

Error message

{cls.__name__}(...) must be called with a collection of some kind, {data} was passed

What it means

Raised by IntervalArray.__new__/_from_sequence when the data argument is a scalar (a single value) rather than a collection. Intervals arrays need a sequence of intervals or bounds; a scalar has no length/shape to build from. TypeError naming the class and the offending value.

Source

Thrown at pandas/core/arrays/interval.py:231

        dtype: Dtype | None = None,
        copy: bool = False,
        verify_integrity: bool = True,
    ) -> Self:
        data = extract_array(data, extract_numpy=True)

        if isinstance(data, cls):
            left: IntervalSide = data._left
            right: IntervalSide = data._right
            closed = closed or data.closed
            dtype = IntervalDtype(left.dtype, closed=closed)
        else:
            # don't allow scalars
            if is_scalar(data):
                msg = (
                    f"{cls.__name__}(...) must be called with a collection "
                    f"of some kind, {data} was passed"
                )
                raise TypeError(msg)

            # might need to convert empty or purely na data
            data = _maybe_convert_platform_interval(data)
            left, right, infer_closed = intervals_to_interval_bounds(
                data, validate_closed=closed is None
            )
            if left.dtype == object:
                left = lib.maybe_convert_objects(left)
                right = lib.maybe_convert_objects(right)
            closed = closed or infer_closed

            left, right, dtype = cls._ensure_simple_new_inputs(
                left,
                right,
                closed=closed,
                copy=copy,
                dtype=dtype,
            )

View on GitHub (pinned to 71959b8cb9)

Solutions

1. Wrap the value in a collection: pd.arrays.IntervalArray([pd.Interval(0, 5)]).
2. For a single interval use pd.Interval directly, not the array constructor.
3. Verify the upstream expression returns a sequence before passing it in.

Example fix

# before
pd.arrays.IntervalArray(pd.Interval(0, 5))
# after
pd.arrays.IntervalArray([pd.Interval(0, 5)])

def to_interval_array(data):
    if pd.api.types.is_scalar(data):
        raise TypeError('IntervalArray requires a collection, wrap the scalar in a list')
    return pd.arrays.IntervalArray(data)

def is_collection(x) -> bool:
    return not pd.api.types.is_scalar(x) and hasattr(x, '__iter__')

When it happens

Trigger: pd.arrays.IntervalArray(pd.Interval(0, 5)); pd.IntervalIndex(0.5); passing a single Interval or number where a list/Series is expected.

Common situations: Forgetting to wrap a single value in a list; refactoring that yields a scalar from a previous step; treating a function meant for arrays as a scalar constructor.

Related errors

• dtype must be an IntervalDtype, got {dtype}
• DatetimeIndex has mixed timezones
• data is already tz-aware {inferred_tz}, unable to set specif
• Cannot construct {type(self).__name__} from scalar data. Pas
• cannot evaluate scalar only bool ops

AI-assisted analysis of pandas-dev/pandas@71959b8cb9 (2026-08-07). Data as JSON: /api/errors/3526585857b74fa1. Report an issue: GitHub.