pandas-dev/pandas · error · ValueError

Wrong dtype: {data.dtype}

Error message

Wrong dtype: {data.dtype}

What it means

Raised by dt64arr_to_periodarr when the input data's dtype is not a datetime64 kind ('M'). The conversion from datetime64 to period ordinals only operates on M-dtyped arrays; integer/object/float inputs are rejected and the caller must convert to datetime64 first.

Source

Thrown at pandas/core/arrays/period.py:1485

    Parameters
    ----------
    data : Union[Series[datetime64[ns]], DatetimeIndex, ndarray[datetime64ns]]
    freq : Optional[Union[str, Tick]]
        Must match the `freq` on the `data` if `data` is a DatetimeIndex
        or Series.
    tz : Optional[tzinfo]

    Returns
    -------
    ordinals : ndarray[int64]
    freq : Tick
        The frequency extracted from the Series or DatetimeIndex if that's
        used.

    """
    if not isinstance(data.dtype, np.dtype) or data.dtype.kind != "M":
        raise ValueError(f"Wrong dtype: {data.dtype}")

    if freq is None:
        if isinstance(data, ABCIndex):
            data, freq = data._values, data.freq
        elif isinstance(data, ABCSeries):
            # freq is always None for DatetimeArray inside a Series, so we
            #  fall back to the inferred freq.
            inferred_freq = data._values._inferred_freq_str
            if inferred_freq is not None:
                warnings.warn(
                    "Constructing PeriodArray from a Series of datetime64 data "
                    "will stop inferring the frequency in a future version. "
                    "Pass `freq` explicitly instead.",
                    Pandas4Warning,
                    stacklevel=find_stack_level(),
                )
                freq = inferred_freq
            data = data._values

View on GitHub (pinned to 71959b8cb9)

Solutions

  1. Convert to datetime64 first: data = pd.to_datetime(data).to_numpy().
  2. Use pd.DatetimeIndex(data) to ensure M dtype before conversion.
  3. If data is ordinals, use PeriodArray directly instead of dt64arr_to_periodarr.

Example fix

# before
import numpy as np
dt64arr_to_periodarr(np.array([18262,18263], dtype='int64'), 'D')
# after
from pandas import to_datetime
dt = to_datetime(['2020-01-01','2020-01-02']).to_numpy()
dt64arr_to_periodarr(dt, 'D')
Defensive patterns

Strategy: validation

Validate before calling

import numpy as np
import pandas as pd

def ensure_datetime64(data):
    if not (isinstance(data.dtype, np.dtype) and data.dtype.kind == 'M'):
        data = pd.to_datetime(data).to_numpy()
    return data

Type guard

import numpy as np

def is_datetime64(data) -> bool:
    return isinstance(getattr(data, 'dtype', None), np.dtype) and data.dtype.kind == 'M'

Try / catch

try:
    ordinals, freq = dt64arr_to_periodarr(data, freq)
except ValueError:
    ordinals, freq = dt64arr_to_periodarr(pd.to_datetime(data).to_numpy(), freq)

Prevention

When it happens

Trigger: dt64arr_to_periodarr(int_array, freq), period_array(numpy_int_array) routed through the datetime64 path, or passing a DatetimeIndex-without-datetime64-dtype edge case.

Common situations: Loading dates as object strings and forgetting to parse. Passing integer epoch values without converting to datetime64. Custom array subclasses whose dtype kind is not M.

Related errors

AI-assisted analysis of pandas-dev/pandas@71959b8cb9 (2026-08-07). Data as JSON: /api/errors/526ccbc654ded056. Report an issue: GitHub.