pandas-dev/pandas · error · TypeError

Not supported to convert IntervalArray to type with…

Error message

Not supported to convert IntervalArray to type with different 'subtype' ({self.dtype.subtype} vs {type.subtype}) and 'closed' ({self.closed} vs {type.closed}) attributes

What it means

Raised in IntervalArray.__arrow_array__ when the caller supplies an explicit 'type' that is an ArrowIntervalType but its subtype and/or closed attributes do not match the inferred interval_type. The target interval attributes must match the source exactly.

Solutions

Match the target subtype and closed to the source's: read arr.dtype.subtype and arr.closed.
Cast the IntervalArray first (astype + set_closed) so attributes align with the requested type.
Omit the 'type' argument to let pandas infer the correct interval type.

Example fix

// before
pyarrow.array(arr, type=ArrowIntervalType(pa.int32(), 'left'))
// after
arr2 = arr.astype('interval[int32]').set_closed('left')
pyarrow.array(arr2, type=ArrowIntervalType(pa.int32(), 'left'))

Defensive patterns

Strategy: validation

Validate before calling

import pyarrow

def arrow_type_matches(arr, arrow_type):
    inferred = pyarrow.from_numpy_dtype(arr.dtype.subtype)
    return (arrow_type.subtype == inferred and arrow_type.closed == arr.closed)

Prevention

Align the target interval's subtype and closed with the source before specifying an explicit arrow type.
Omit the 'type' argument to let pandas infer the correct interval type.
Cast (astype + set_closed) the source to match the schema when needed.

When it happens

Trigger: pyarrow.array(arr, type=ArrowIntervalType(pa.int32(), 'left')) when arr is int64/right; specifying a mismatched closed value during arrow conversion.

Common situations: Explicit type specification during pyarrow conversion that does not align with the source; schema-driven conversion with a fixed interval type.

Related errors

AI-assisted analysis of pandas-dev/pandas@3b7651241d (2026-08-11). Data as JSON: /api/errors/000b116235278e0d. Report an issue: GitHub.

Appendix: source

Thrown at pandas/core/arrays/interval.py:1625

        )
        mask = self.isna()
        if mask.any():
            # if there are missing values, set validity bitmap also on the array level
            null_bitmap = pyarrow.array(~mask).buffers()[1]
            storage_array = pyarrow.StructArray.from_buffers(
                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.

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