What causes "Incorrect dtype" in pandas-dev/pandas?

Convert the Series to period dtype first: s.astype('period[D]').

pandas-dev/pandas · error · TypeError

Incorrect dtype

Error message

Incorrect dtype

What it means

Raised by PeriodArray.__init__ when values is a pandas Series whose underlying _values is not itself a PeriodArray. The constructor will only adopt a Series if its values are already period-typed; otherwise the Series does not carry valid period ordinals and is rejected as incorrectly typed.

Source

Thrown at pandas/core/arrays/period.py:234

        _field_ops + _object_ops + _bool_ops + _deprecated_ops
    )
    _datetimelike_methods: list[str] = ["strftime", "to_timestamp", "asfreq"]

    _dtype: PeriodDtype

    # --------------------------------------------------------------------
    # Constructors

    def __init__(self, values, dtype: Dtype | None = None, copy: bool = False) -> None:
        if dtype is not None:
            dtype = pandas_dtype(dtype)
            if not isinstance(dtype, PeriodDtype):
                raise ValueError(f"Invalid dtype {dtype} for PeriodArray")

        if isinstance(values, ABCSeries):
            values = values._values
            if not isinstance(values, type(self)):
                raise TypeError("Incorrect dtype")

        elif isinstance(values, ABCPeriodIndex):
            values = values._values

        if isinstance(values, type(self)):
            if dtype is not None and dtype != values.dtype:
                raise raise_on_incompatible(values, dtype.freq)
            values, dtype = values._ndarray, values.dtype

        if not copy:
            values = np.asarray(values, dtype="int64")
        else:
            values = np.array(values, dtype="int64", copy=copy)
        if dtype is None:
            raise ValueError("dtype is not specified and cannot be inferred")
        dtype = cast("PeriodDtype", dtype)
        NDArrayBacked.__init__(self, values, dtype)

View on GitHub (pinned to 71959b8cb9)

Solutions

Convert the Series to period dtype first: s.astype('period[D]').
Use pd.period_array(s.tolist(), dtype=pd.PeriodDtype('D')) to build from arbitrary values.
Ensure the Series was created from a PeriodIndex or period-typed data.

Example fix

# before
s = pd.Series(['2020-01-01','2020-01-02'])
pa = pd.arrays.PeriodArray(s)
# after
pa = s.astype('period[D]').array
# or
pa = pd.period_array(s, dtype=pd.PeriodDtype('D'))

Defensive patterns

Strategy: validation

Validate before calling

import pandas as pd

def period_array_from_series(s: pd.Series, freq: str = 'D'):
    if not isinstance(s._values, pd.arrays.PeriodArray):
        s = s.astype(f'period[{freq}]')
    return s.array

Type guard

import pandas as pd

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

Try / catch

try:
    pa = pd.arrays.PeriodArray(s)
except TypeError:
    pa = pd.period_array(s, dtype=pd.PeriodDtype('D'))

Prevention

Never pass a non-period Series to PeriodArray(); convert with .astype first.
Use pd.period_array(data, dtype=...) for arbitrary input.
Check s.dtype is PeriodDtype before constructing from a Series.

When it happens

Trigger: Passing pd.arrays.PeriodArray(some_series) where some_series.dtype is not period[M]/period[D]/etc. Building a PeriodArray from a Series of strings or ints without going through _from_sequence.

Common situations: Users wrapping an arbitrary Series into PeriodArray directly. Series that came from .astype('object') or arithmetic that lost period dtype. Confusing PeriodArray(Series) with pd.Series.astype('period[...]').

Related errors

AI-assisted analysis of pandas-dev/pandas@71959b8cb9 (2026-08-07). Data as JSON: /api/errors/73c400730efef40b. Report an issue: GitHub.