pandas-dev/pandas · error · TypeError

'std' and 'sem' are not valid for PeriodDtype

Error message

'std' and 'sem' are not valid for PeriodDtype

What it means

Raised inside the std/sem branch of the groupby reduce path when self.dtype is a PeriodDtype. Standard deviation and standard error require arithmetic on magnitudes, but a Period is an ordinal label tied to a frequency, so dispersion statistics are undefined. The guard fires after the ordinal computation but before attempting to build the result.

Source

Thrown at pandas/core/arrays/datetimelike.py:1675

            min_count=min_count,
            ngroups=ngroups,
            comp_ids=ids,
            mask=None,
            **kwargs,
        )

        if op.how in op.cast_blocklist:
            # i.e. how in ["rank"], since other cast_blocklist methods don't go
            #  through cython_operation
            return res_values

        # We did a view to M8[ns] above, now we go the other direction
        assert res_values.dtype == "M8[ns]"
        if how in ["std", "sem"]:
            from pandas.core.arrays import TimedeltaArray

            if isinstance(self.dtype, PeriodDtype):
                raise TypeError("'std' and 'sem' are not valid for PeriodDtype")
            self = cast("DatetimeArray | TimedeltaArray", self)
            new_dtype = f"m8[{self.unit}]"
            res_values = res_values.view(new_dtype)
            return TimedeltaArray._simple_new(res_values, dtype=res_values.dtype)

        res_values = res_values.view(self._ndarray.dtype)
        return self._from_backing_data(res_values)

    def _groupby_quantile(
        self,
        *,
        qs: npt.NDArray[np.float64],
        interpolation: Literal["linear", "lower", "higher", "nearest", "midpoint"],
        ids: npt.NDArray[np.intp],
        ngroups: int,
        starts: npt.NDArray[np.int64],
        ends: npt.NDArray[np.int64],
    ) -> ArrayLike:

View on GitHub (pinned to 71959b8cb9)

Solutions

1. Drop std/sem from the aggregation for period columns.
2. If you need spread, convert to an ordinal integer with .astype('int64') (the period ordinal) or to Timestamp with .to_timestamp() and compute on the resulting datetime values.
3. Use .value_counts() or nunique() for period-distribution summaries instead.

Example fix

# before
pd.period_range('2020-01','2020-05',freq='M').to_series().std()

# after
ordinals = pd.period_range('2020-01','2020-05',freq='M').astype('int64')
ordinals.std()

if pd.api.types.is_period_dtype(s) and any(op in {'std','sem'} for op in ops):
    raise ValueError('Period columns do not support std/sem')

def is_period_column(s) -> bool:
    return isinstance(s.dtype, pd.PeriodDtype)

try:
    s.std()
except TypeError as e:
    if 'not valid for PeriodDtype' in str(e):
        s.astype('int64').std()
    else: raise

When it happens

Trigger: Calling .std() or .sem() on a PeriodIndex, PeriodArray, or a Series of Period dtype, or df.groupby('g')['period_col'].std(). Also reachable via Series.agg(['mean','std']) on a period column.

Common situations: Running describe()/agg() pipelines over period-encoded data (monthly reporting periods, fiscal quarters, hour-of-week periods). Treating a Period column like a datetime in a stats template.

Related errors

• Period type does not support {how} operations
• timedelta64 type does not support {how} operations
• mean is not implemented for {type(self).__name__} since the
• datetime64 type does not support operation '{how}'
• '{how}' with PeriodDtype is no longer supported. Use (obj !=

AI-assisted analysis of pandas-dev/pandas@71959b8cb9 (2026-08-07). Data as JSON: /api/errors/bce5cbfda5de70b6. Report an issue: GitHub.