pandas-dev/pandas · error · TypeError

Not supported to convert IntervalArray to

Error message

Not supported to convert IntervalArray to '{type}' type

What it means

Raised in IntervalArray.__arrow_array__ when the explicit 'type' is not None, does not equal the storage_type, and is not an ArrowIntervalType. Generic non-interval Arrow types are not accepted for interval data.

Solutions

  1. Omit the 'type' argument and let pandas infer the interval type.
  2. Pass a matching ArrowIntervalType if an explicit type is required.
  3. Convert endpoints to the desired primitive type separately if you truly need non-interval output.

Example fix

// before
pyarrow.array(arr, type=pa.string())
// after
pyarrow.array(arr)  # let pandas infer interval type
Defensive patterns

Strategy: type-guard

Validate before calling

from pandas.core.arrays.arrow.extension_types import ArrowIntervalType

def is_valid_interval_arrow_type(t):
    return t is None or isinstance(t, ArrowIntervalType)

Type guard

from pandas.core.arrays.arrow.extension_types import ArrowIntervalType

def is_arrow_interval_type(t):
    return isinstance(t, ArrowIntervalType)

Prevention

When it happens

Trigger: pyarrow.array(arr, type=pa.int64()); pyarrow.array(arr, type=pa.string()); pyarrow.array(arr, type=pa.list_(pa.int64())).

Common situations: Passing a generic primitive arrow type where an interval type is required; schema reuse across non-interval columns.

Related errors

AI-assisted analysis of pandas-dev/pandas@3b7651241d (2026-08-11). Data as JSON: /api/errors/e1b57327ba272b11. Report an issue: GitHub.

Appendix: source

Thrown at pandas/core/arrays/interval.py:1631

                storage_array.type,
                len(storage_array),
                [null_bitmap],
                children=[storage_array.field(0), storage_array.field(1)],
            )

        if type is not None:
            if type.equals(interval_type.storage_type):
                return storage_array
            elif isinstance(type, ArrowIntervalType):
                # ensure we have the same subtype and closed attributes
                if not type.equals(interval_type):
                    raise TypeError(
                        "Not supported to convert IntervalArray to type with "
                        f"different 'subtype' ({self.dtype.subtype} vs {type.subtype}) "
                        f"and 'closed' ({self.closed} vs {type.closed}) attributes"
                    )
            else:
                raise TypeError(
                    f"Not supported to convert IntervalArray to '{type}' type"
                )

        return pyarrow.ExtensionArray.from_storage(interval_type, storage_array)

    def to_tuples(self, na_tuple: bool = True) -> np.ndarray:
        """
        Return an ndarray (if self is IntervalArray) or Index \
        (if self is IntervalIndex) of tuples of the form (left, right).

        This method extracts the bounds of each interval as a tuple,
        useful for iteration or conversion to other data structures.

        Parameters
        ----------
        na_tuple : bool, default True
            If ``True``, return ``NA`` as a tuple ``(nan, nan)``. If ``False``,
            just return ``NA`` as ``nan``.

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