What causes "Not supported to convert PeriodArray to '{type}' type" in pandas-dev/pandas?

Omit the type argument and let pandas infer ArrowPeriodType.

pandas-dev/pandas · error · TypeError

Not supported to convert PeriodArray to

Error message

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

What it means

Raised by PeriodArray.__arrow_array__ when the target pyarrow type is neither an integer type nor an ArrowPeriodType. Period data can only be exported to arrow as raw int64 ordinals or as a proper period extension type; other types (string, timestamp, float, list, etc.) are not supported and would lose semantics.

Solutions

Omit the type argument and let pandas infer ArrowPeriodType.
Specify an integer type if you want raw ordinals: pa.array(period_idx, type=pa.int64()).
If a timestamp/string representation is required, convert in pandas first (.to_timestamp() or .astype(str)) then export.

Example fix

# before
pa.array(period_idx, type=pa.timestamp('ns'))  # raises

# after
pa.array(period_idx.to_timestamp())

Defensive patterns

Strategy: validation

Validate before calling

import pyarrow as pa
from pandas.core.arrays.arrow.extension_types import ArrowPeriodType

def supported_arrow_type_for_period(target_type):
    if target_type is None:
        return True
    if pa.types.is_integer(target_type):
        return True
    return isinstance(target_type, ArrowPeriodType)

Type guard

def is_supported_period_arrow_type(target_type) -> bool:
    import pyarrow as pa
    from pandas.core.arrays.arrow.extension_types import ArrowPeriodType
    return (target_type is None or pa.types.is_integer(target_type)
            or isinstance(target_type, ArrowPeriodType))

Try / catch

try:
    pa.array(period_idx, type=target_type)
except TypeError as e:
    if 'Not supported to convert' in str(e):
        pa.array(period_idx.to_timestamp())  # or omit type
    else:
        raise

Prevention

Omit the pyarrow type argument to let pandas pick ArrowPeriodType automatically.
For raw ordinals, use pa.int64(); for timestamps, convert via .to_timestamp() first.
Validate the schema's period-column type in from_pandas pipelines.

When it happens

Trigger: pa.array(period_idx, type=pa.string()). pa.array(period_idx, type=pa.timestamp('ns')). pyarrow.Table.from_pandas with a schema that declares the period column as a non-integer/period type.

Common situations: Schema mismatches in from_pandas/to_pandas round-trips; assuming period maps to arrow timestamp; user-built pyarrow schemas.

Related errors

AI-assisted analysis of pandas-dev/pandas@3b7651241d (2026-08-11). Data as JSON: /api/errors/eff81327dbd244db. Report an issue: GitHub.

Appendix: source

Thrown at pandas/core/arrays/period.py:491

        """
        Convert myself into a pyarrow Array.
        """
        import pyarrow

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

        if type is not None:
            if pyarrow.types.is_integer(type):
                return pyarrow.array(self._ndarray, mask=self.isna(), type=type)
            elif isinstance(type, ArrowPeriodType):
                # ensure we have the same freq
                if self.freqstr != type.freq:
                    raise TypeError(
                        "Not supported to convert PeriodArray to array with different "
                        f"'freq' ({self.freqstr} vs {type.freq})"
                    )
            else:
                raise TypeError(
                    f"Not supported to convert PeriodArray to '{type}' type"
                )

        period_type = ArrowPeriodType(self.freqstr)
        storage_array = pyarrow.array(self._ndarray, mask=self.isna(), type="int64")
        return pyarrow.ExtensionArray.from_storage(period_type, storage_array)

    # --------------------------------------------------------------------
    # Vectorized analogues of Period properties

    year = _field_accessor(
        "year",
        """
        The year of the period.

        Returns the year component for each period in the index.

        See Also

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