pandas-dev/pandas · error · TypeError

Cannot add or subtract timedelta64[ns] dtype from {self.dtyp

Error message

Cannot add or subtract timedelta64[ns] dtype from {self.dtype}

What it means

Raised by PeriodArray._add_timedelta_arraylike when self.dtype._is_tick_like() is False. Adding/subtracting a timedelta only makes sense for period arrays whose freq is a Tick (ns, us, ms, s, min, h, D); non-tick freqs (M, Q, Y, W) have variable-length periods, so timedelta arithmetic is undefined and rejected.

Source

Thrown at pandas/core/arrays/period.py:1258

        else:
            td = np.asarray(Timedelta(other).asm8)
        return self._add_timedelta_arraylike(td)

    def _add_timedelta_arraylike(
        self, other: TimedeltaArray | npt.NDArray[np.timedelta64]
    ) -> Self:
        """
        Parameters
        ----------
        other : TimedeltaArray or ndarray[timedelta64]

        Returns
        -------
        PeriodArray
        """
        if not self.dtype._is_tick_like():
            # We cannot add timedelta-like to non-tick PeriodArray
            raise TypeError(
                f"Cannot add or subtract timedelta64[ns] dtype from {self.dtype}"
            )

        dtype = np.dtype(f"m8[{self.dtype._td64_unit}]")

        # Similar to _check_timedeltalike_freq_compat, but we raise with a
        #  more specific exception message if necessary.
        try:
            delta = astype_overflowsafe(
                np.asarray(other), dtype=dtype, copy=False, round_ok=False
            )
        except ValueError as err:
            # e.g. if we have minutes freq and try to add 30s
            # "Cannot losslessly convert units"
            raise IncompatibleFrequency(
                "Cannot add/subtract timedelta-like from PeriodArray that is "
                "not an integer multiple of the PeriodArray's freq."
            ) from err

View on GitHub (pinned to 71959b8cb9)

Solutions

Use asfreq to a tick freq first, then add: pa.asfreq('D') + timedelta.
Add Period-freq multiples: pa + n * pa.freq (integer multiples of the period).
Convert to timestamps: pa.to_timestamp() + timedelta for wall-clock arithmetic.

Example fix

# before
pa = pd.period_range('2020-01','2020-03', freq='M')._data
pa + pd.Timedelta(days=1)
# after
pa.asfreq('D') + pd.Timedelta(days=1)
# or shift by whole periods
pa + 1  # shifts one month

Defensive patterns

Strategy: type-guard

Validate before calling

import pandas as pd
from pandas._libs.tslibs.offsets import Tick

def can_add_timedelta(pa) -> bool:
    return isinstance(pa.freq, (Tick,)) or pa.freq.rule_code == 'D'

Type guard

from pandas._libs.tslibs.offsets import Tick

def is_tick_freq(pa) -> bool:
    return pa.dtype._is_tick_like()

Try / catch

try:
    out = pa + td
except TypeError:
    out = pa.asfreq('D') + td

Prevention

Check pa.dtype._is_tick_like() before timedelta arithmetic.
Use integer-multiple shifts (pa + n) for non-tick period arrays.
Convert to timestamps for wall-clock timedelta math.

When it happens

Trigger: period_range(..., freq='M') + pd.Timedelta(days=1); a Series of period[M] plus a timedelta; vectorized period[Tick] arithmetic where the freq slipped to a non-tick.

Common situations: Monthly/quarterly/annual period columns where users try timedelta math. Mixing datetime arithmetic idioms with period data. Aggregations that change the freq to month then apply offset shifts.

Related errors

AI-assisted analysis of pandas-dev/pandas@71959b8cb9 (2026-08-07). Data as JSON: /api/errors/ebf3801ff4760837. Report an issue: GitHub.