pandas-dev/pandas · error · NotImplementedError

{pa_type}

Error message

{pa_type}

What it means

In _mode, temporal pyarrow types are cast to int32 (32-bit) or int64 (64-bit) before value_counts. The branch explicitly raises NotImplementedError(pa_type) for any other temporal bit width. As of writing, pyarrow only produces 32- and 64-bit temporal types, so this is a forward-compatible guard against new pyarrow temporal widths (e.g. a future narrower date/time type).

Source

Thrown at pandas/core/arrays/arrow/array.py:3133

        Parameters
        ----------
        dropna : bool, default True
            Don't consider counts of NA values.

        Returns
        -------
        same type as self
            Sorted, if possible.
        """
        pa_type = self._pa_array.type
        if pa.types.is_temporal(pa_type):
            nbits = pa_type.bit_width
            if nbits == 32:
                data = self._pa_array.cast(pa.int32())
            elif nbits == 64:
                data = self._pa_array.cast(pa.int64())
            else:
                raise NotImplementedError(pa_type)
        else:
            data = self._pa_array

        if dropna:
            data = data.drop_null()

        res = pc.value_counts(data)
        most_common = res.field("values").filter(
            pc.equal(res.field("counts"), pc.max(res.field("counts")))
        )

        if pa.types.is_temporal(pa_type):
            most_common = most_common.cast(pa_type)

        most_common = most_common.take(pc.array_sort_indices(most_common))
        return self._from_pyarrow_array(most_common)

    def _validate_setitem_value(self, value):

View on GitHub (pinned to 3b7651241d)

Solutions

1. Cast the column to a standard 64-bit temporal dtype (e.g. timestamp[ns]) before calling .mode().
2. Upgrade or downgrade pyarrow to a version whose temporal types are 32/64-bit only.
3. Compute mode manually via .value_counts() after casting to int64.

Example fix

// before
s = pd.Series([...], dtype="timestamp[unit][pyarrow]")  # exotic width
s.mode()
// after
s = s.astype("timestamp[ns][pyarrow]")
s.mode()

import pyarrow as pa

def mode_supported(arr) -> bool:
    t = arr._pa_array.type
    if pa.types.is_temporal(t):
        return t.bit_width in (32, 64)
    return True

import pyarrow as pa

def is_standard_temporal(arr) -> bool:
    t = arr._pa_array.type
    return not pa.types.is_temporal(t) or t.bit_width in (32, 64)

try:
    s.mode()
except NotImplementedError:
    s.astype("timestamp[ns][pyarrow]").mode()

When it happens

Trigger: Computing .mode() on an ArrowExtensionArray whose pyarrow type is temporal with a bit_width other than 32 or 64 (currently not producible by stock pyarrow).

Common situations: Future pyarrow release introducing a new temporal width; custom pyarrow extension types registered as temporal.

Related errors

• Converting strings to {pa_type} is not implemented.
• {type(self)} does not support reshape as backed by a 1D pyar
• interpolate is not implemented for dtype={self.dtype}
• Invalid side: {side}. Side must be one of 'left', 'right', '
• invalid normalization form

AI-assisted analysis of pandas-dev/pandas@3b7651241d (2026-08-11). Data as JSON: /api/errors/cbf495839c857683. Report an issue: GitHub.