pandas-dev/pandas · error · TypeError

Passing PeriodDtype data is invalid. Use `data.to_timestamp(

Error message

Passing PeriodDtype data is invalid. Use `data.to_timestamp()` instead

What it means

Raised by maybe_convert_dtype when the data carries a PeriodDtype. Periods are discrete time spans and are not interchangeable with datetime64 instants; pandas directs the user to Period.to_timestamp(). TypeError.

Source

Thrown at pandas/core/arrays/datetimes.py:2907

    if not hasattr(data, "dtype"):
        # e.g. collections.deque
        return data, copy

    if is_float_dtype(data.dtype):
        # pre-2.0 we treated these as wall-times, inconsistent with ints
        # GH#23675, GH#45573 deprecated to treat symmetrically with integer dtypes.
        # Note: data.astype(np.int64) fails ARM tests, see
        # https://github.com/pandas-dev/pandas/issues/49468.
        data = data.astype(DT64NS_DTYPE).view("i8")
        copy = False

    elif lib.is_np_dtype(data.dtype, "m") or is_bool_dtype(data.dtype):
        # GH#29794 enforcing deprecation introduced in GH#23539
        raise TypeError(f"dtype {data.dtype} cannot be converted to datetime64[ns]")
    elif isinstance(data.dtype, PeriodDtype):
        # Note: without explicitly raising here, PeriodIndex
        #  test_setops.test_join_does_not_recur fails
        raise TypeError(
            "Passing PeriodDtype data is invalid. Use `data.to_timestamp()` instead"
        )

    elif isinstance(data.dtype, ExtensionDtype) and not isinstance(
        data.dtype, DatetimeTZDtype
    ):
        # TODO: We have no tests for these
        data = np.array(data, dtype=np.object_)
        copy = False

    return data, copy


# -------------------------------------------------------------------
# Validation and Inference


def _maybe_infer_tz(tz: tzinfo | None, inferred_tz: tzinfo | None) -> tzinfo | None:

View on GitHub (pinned to 71959b8cb9)

Solutions

1. Use period_index.to_timestamp() (or series.dt.to_timestamp()) to convert periods to datetimes.
2. Keep Period data as PeriodIndex when you need period semantics.

Example fix

# before
pd.DatetimeIndex(period_index)
# after
period_index.to_timestamp()

def to_datetime_safe(data):
    if isinstance(getattr(data, 'dtype', None), pd.PeriodDtype):
        return data.to_timestamp()
    return pd.DatetimeIndex(data)

def is_period(data) -> bool:
    return isinstance(getattr(data, 'dtype', None), pd.PeriodDtype)

When it happens

Trigger: pd.DatetimeIndex(period_index) or pd.to_datetime(period_series); passing a Series with dtype period[...] into a datetime constructor.

Common situations: Pipeline that converts PeriodIndex to DatetimeIndex by re-wrapping instead of using the canonical method.

Related errors

• You must pass a freq argument as current index has none.
• dtype {data.dtype} cannot be converted to datetime64[ns]
• Passing in 'datetime64' dtype with no precision is not allow
• Unexpected value for 'dtype': '{dtype}'. Must be 'datetime64
• cannot supply both a tz and a dtype with a tz

AI-assisted analysis of pandas-dev/pandas@71959b8cb9 (2026-08-07). Data as JSON: /api/errors/e247a56b1072b74e. Report an issue: GitHub.