period.py

from __future__ import annotations

from datetime import timedelta
import operator
from typing import (
    TYPE_CHECKING,
    Any,
    Literal,
    TypeVar,
    cast,
    overload,
)
import warnings

import numpy as np

from pandas._libs import (
    algos as libalgos,
    lib,
)
from pandas._libs.arrays import NDArrayBacked
from pandas._libs.tslibs import (
    BaseOffset,
    NaT,
    NaTType,
    Timedelta,
    add_overflowsafe,
    astype_overflowsafe,
    dt64arr_to_periodarr as c_dt64arr_to_periodarr,
    get_unit_from_dtype,
    iNaT,
    parsing,
    period as libperiod,
    to_offset,
)
from pandas._libs.tslibs.dtypes import (
    FreqGroup,
    PeriodDtypeBase,
)
from pandas._libs.tslibs.fields import isleapyear_arr
from pandas._libs.tslibs.offsets import (
    Tick,
    delta_to_tick,
)
from pandas._libs.tslibs.period import (
    DIFFERENT_FREQ,
    IncompatibleFrequency,
    Period,
    get_period_field_arr,
    period_asfreq_arr,
)
from pandas.util._decorators import (
    cache_readonly,
    doc,
)

from pandas.core.dtypes.common import (
    ensure_object,
    pandas_dtype,
)
from pandas.core.dtypes.dtypes import (
    DatetimeTZDtype,
    PeriodDtype,
)
from pandas.core.dtypes.generic import (
    ABCIndex,
    ABCPeriodIndex,
    ABCSeries,
    ABCTimedeltaArray,
)
from pandas.core.dtypes.missing import isna

from pandas.core.arrays import datetimelike as dtl
import pandas.core.common as com

if TYPE_CHECKING:
    from collections.abc import (
        Callable,
        Sequence,
    )

    from pandas._typing import (
        AnyArrayLike,
        Dtype,
        FillnaOptions,
        NpDtype,
        NumpySorter,
        NumpyValueArrayLike,
        Self,
        npt,
    )

    from pandas.core.dtypes.dtypes import ExtensionDtype

    from pandas.core.arrays import (
        DatetimeArray,
        TimedeltaArray,
    )
    from pandas.core.arrays.base import ExtensionArray


BaseOffsetT = TypeVar("BaseOffsetT", bound=BaseOffset)


_shared_doc_kwargs = {
    "klass": "PeriodArray",
}


def _field_accessor(name: str, docstring: str | None = None):
    def f(self):
        base = self.dtype._dtype_code
        result = get_period_field_arr(name, self.asi8, base)
        return result

    f.__name__ = name
    f.__doc__ = docstring
    return property(f)


# error: Definition of "_concat_same_type" in base class "NDArrayBacked" is
# incompatible with definition in base class "ExtensionArray"
class PeriodArray(dtl.DatelikeOps, libperiod.PeriodMixin):  # type: ignore[misc]
    """
    Pandas ExtensionArray for storing Period data.

    Users should use :func:`~pandas.array` to create new instances.

    Parameters
    ----------
    values : Union[PeriodArray, Series[period], ndarray[int], PeriodIndex]
        The data to store. These should be arrays that can be directly
        converted to ordinals without inference or copy (PeriodArray,
        ndarray[int64]), or a box around such an array (Series[period],
        PeriodIndex).
    dtype : PeriodDtype, optional
        A PeriodDtype instance from which to extract a `freq`. If both
        `freq` and `dtype` are specified, then the frequencies must match.
    copy : bool, default False
        Whether to copy the ordinals before storing.

    Attributes
    ----------
    None

    Methods
    -------
    None

    See Also
    --------
    Period: Represents a period of time.
    PeriodIndex : Immutable Index for period data.
    period_range: Create a fixed-frequency PeriodArray.
    array: Construct a pandas array.

    Notes
    -----
    There are two components to a PeriodArray

    - ordinals : integer ndarray
    - freq : pd.tseries.offsets.Offset

    The values are physically stored as a 1-D ndarray of integers. These are
    called "ordinals" and represent some kind of offset from a base.

    The `freq` indicates the span covered by each element of the array.
    All elements in the PeriodArray have the same `freq`.

    Examples
    --------
    >>> pd.arrays.PeriodArray(pd.PeriodIndex(["2023-01-01", "2023-01-02"], freq="D"))
    <PeriodArray>
    ['2023-01-01', '2023-01-02']
    Length: 2, dtype: period[D]
    """

    # array priority higher than numpy scalars
    __array_priority__ = 1000
    _typ = "periodarray"  # ABCPeriodArray
    _internal_fill_value = np.int64(iNaT)
    _recognized_scalars = (Period,)
    _is_recognized_dtype = lambda x: isinstance(
        x, PeriodDtype
    )  # check_compatible_with checks freq match
    _infer_matches = ("period",)

    @property
    def _scalar_type(self) -> type[Period]:
        return Period

    # Names others delegate to us
    _other_ops: list[str] = []
    _bool_ops: list[str] = ["is_leap_year"]
    _object_ops: list[str] = ["start_time", "end_time", "freq"]
    _field_ops: list[str] = [
        "year",
        "month",
        "day",
        "hour",
        "minute",
        "second",
        "weekofyear",
        "weekday",
        "week",
        "dayofweek",
        "day_of_week",
        "dayofyear",
        "day_of_year",
        "quarter",
        "qyear",
        "days_in_month",
        "daysinmonth",
    ]
    _datetimelike_ops: list[str] = _field_ops + _object_ops + _bool_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)

    # error: Signature of "_simple_new" incompatible with supertype "NDArrayBacked"
    @classmethod
    def _simple_new(  # type: ignore[override]
        cls,
        values: npt.NDArray[np.int64],
        dtype: PeriodDtype,
    ) -> Self:
        # alias for PeriodArray.__init__
        assertion_msg = "Should be numpy array of type i8"
        assert isinstance(values, np.ndarray) and values.dtype == "i8", assertion_msg
        return cls(values, dtype=dtype)

    @classmethod
    def _from_sequence(
        cls,
        scalars,
        *,
        dtype: Dtype | None = None,
        copy: bool = False,
    ) -> Self:
        if dtype is not None:
            dtype = pandas_dtype(dtype)
        if dtype and isinstance(dtype, PeriodDtype):
            freq = dtype.freq
        else:
            freq = None

        if isinstance(scalars, cls):
            validate_dtype_freq(scalars.dtype, freq)
            if copy:
                scalars = scalars.copy()
            return scalars

        periods = np.asarray(scalars, dtype=object)

        freq = freq or libperiod.extract_freq(periods)
        ordinals = libperiod.extract_ordinals(periods, freq)
        dtype = PeriodDtype(freq)
        return cls(ordinals, dtype=dtype)

    @classmethod
    def _from_sequence_of_strings(
        cls, strings, *, dtype: ExtensionDtype, copy: bool = False
    ) -> Self:
        return cls._from_sequence(strings, dtype=dtype, copy=copy)

    @classmethod
    def _from_datetime64(cls, data, freq, tz=None) -> Self:
        """
        Construct a PeriodArray from a datetime64 array

        Parameters
        ----------
        data : ndarray[datetime64[ns], datetime64[ns, tz]]
        freq : str or Tick
        tz : tzinfo, optional

        Returns
        -------
        PeriodArray[freq]
        """
        if isinstance(freq, BaseOffset):
            freq = PeriodDtype(freq)._freqstr
        data, freq = dt64arr_to_periodarr(data, freq, tz)
        dtype = PeriodDtype(freq)
        return cls(data, dtype=dtype)

    @classmethod
    def _generate_range(cls, start, end, periods, freq):
        periods = dtl.validate_periods(periods)

        if freq is not None:
            freq = Period._maybe_convert_freq(freq)

        if start is not None or end is not None:
            subarr, freq = _get_ordinal_range(start, end, periods, freq)
        else:
            raise ValueError("Not enough parameters to construct Period range")

        return subarr, freq

    @classmethod
    def _from_fields(cls, *, fields: dict, freq) -> Self:
        subarr, freq = _range_from_fields(freq=freq, **fields)
        dtype = PeriodDtype(freq)
        return cls._simple_new(subarr, dtype=dtype)

    # -----------------------------------------------------------------
    # DatetimeLike Interface

    # error: Argument 1 of "_unbox_scalar" is incompatible with supertype
    # "DatetimeLikeArrayMixin"; supertype defines the argument type as
    # "Union[Union[Period, Any, Timedelta], NaTType]"
    def _unbox_scalar(  # type: ignore[override]
        self,
        value: Period | NaTType,
    ) -> np.int64:
        if value is NaT:
            # error: Item "Period" of "Union[Period, NaTType]" has no attribute "value"
            return np.int64(value._value)  # type: ignore[union-attr]
        elif isinstance(value, self._scalar_type):
            self._check_compatible_with(value)
            return np.int64(value.ordinal)
        else:
            raise ValueError(f"'value' should be a Period. Got '{value}' instead.")

    def _scalar_from_string(self, value: str) -> Period:
        return Period(value, freq=self.freq)

    # error: Argument 1 of "_check_compatible_with" is incompatible with
    # supertype "DatetimeLikeArrayMixin"; supertype defines the argument type
    # as "Period | Timestamp | Timedelta | NaTType"
    def _check_compatible_with(self, other: Period | NaTType | PeriodArray) -> None:  # type: ignore[override]
        if other is NaT:
            return
        # error: Item "NaTType" of "Period | NaTType | PeriodArray" has no
        # attribute "freq"
        self._require_matching_freq(other.freq)  # type: ignore[union-attr]

    # --------------------------------------------------------------------
    # Data / Attributes

    @cache_readonly
    def dtype(self) -> PeriodDtype:
        return self._dtype

    # error: Cannot override writeable attribute with read-only property
    @property  # type: ignore[override]
    def freq(self) -> BaseOffset:
        """
        Return the frequency object for this PeriodArray.
        """
        return self.dtype.freq

    @property
    def freqstr(self) -> str:
        return PeriodDtype(self.freq)._freqstr

    def __array__(
        self, dtype: NpDtype | None = None, copy: bool | None = None
    ) -> np.ndarray:
        if dtype == "i8":
            return self.asi8
        elif dtype == bool:
            return ~self._isnan

        # This will raise TypeError for non-object dtypes
        return np.array(list(self), dtype=object)

    def __arrow_array__(self, type=None):
        """
        Convert myself into a pyarrow Array.
        """
        import pyarrow

        from pandas.core.arrays.arrow.extension_types import ArrowPeriodType

        if type is not None:
            if pyarrow.types.is_integer(type):
                return pyarrow.array(self._ndarray, mask=self.isna(), type=type)
            elif isinstance(type, ArrowPeriodType):
                # ensure we have the same freq
                if self.freqstr != type.freq:
                    raise TypeError(
                        "Not supported to convert PeriodArray to array with different "
                        f"'freq' ({self.freqstr} vs {type.freq})"
                    )
            else:
                raise TypeError(
                    f"Not supported to convert PeriodArray to '{type}' type"
                )

        period_type = ArrowPeriodType(self.freqstr)
        storage_array = pyarrow.array(self._ndarray, mask=self.isna(), type="int64")
        return pyarrow.ExtensionArray.from_storage(period_type, storage_array)

    # --------------------------------------------------------------------
    # Vectorized analogues of Period properties

    year = _field_accessor(
        "year",
        """
        The year of the period.

        See Also
        --------
        PeriodIndex.day_of_year : The ordinal day of the year.
        PeriodIndex.dayofyear : The ordinal day of the year.
        PeriodIndex.is_leap_year : Logical indicating if the date belongs to a
            leap year.
        PeriodIndex.weekofyear : The week ordinal of the year.
        PeriodIndex.year : The year of the period.

        Examples
        --------
        >>> idx = pd.PeriodIndex(["2023", "2024", "2025"], freq="Y")
        >>> idx.year
        Index([2023, 2024, 2025], dtype='int64')
        """,
    )
    month = _field_accessor(
        "month",
        """
        The month as January=1, December=12.

        See Also
        --------
        PeriodIndex.days_in_month : The number of days in the month.
        PeriodIndex.daysinmonth : The number of days in the month.

        Examples
        --------
        >>> idx = pd.PeriodIndex(["2023-01", "2023-02", "2023-03"], freq="M")
        >>> idx.month
        Index([1, 2, 3], dtype='int64')
        """,
    )
    day = _field_accessor(
        "day",
        """
        The days of the period.

        See Also
        --------
        PeriodIndex.day_of_week : The day of the week with Monday=0, Sunday=6.
        PeriodIndex.day_of_year : The ordinal day of the year.
        PeriodIndex.dayofweek : The day of the week with Monday=0, Sunday=6.
        PeriodIndex.dayofyear : The ordinal day of the year.
        PeriodIndex.days_in_month : The number of days in the month.
        PeriodIndex.daysinmonth : The number of days in the month.
        PeriodIndex.weekday : The day of the week with Monday=0, Sunday=6.

        Examples
        --------
        >>> idx = pd.PeriodIndex(['2020-01-31', '2020-02-28'], freq='D')
        >>> idx.day
        Index([31, 28], dtype='int64')
        """,
    )
    hour = _field_accessor(
        "hour",
        """
        The hour of the period.

        See Also
        --------
        PeriodIndex.minute : The minute of the period.
        PeriodIndex.second : The second of the period.
        PeriodIndex.to_timestamp : Cast to DatetimeArray/Index.

        Examples
        --------
        >>> idx = pd.PeriodIndex(["2023-01-01 10:00", "2023-01-01 11:00"], freq='h')
        >>> idx.hour
        Index([10, 11], dtype='int64')
        """,
    )
    minute = _field_accessor(
        "minute",
        """
        The minute of the period.

        See Also
        --------
        PeriodIndex.hour : The hour of the period.
        PeriodIndex.second : The second of the period.
        PeriodIndex.to_timestamp : Cast to DatetimeArray/Index.

        Examples
        --------
        >>> idx = pd.PeriodIndex(["2023-01-01 10:30:00",
        ...                       "2023-01-01 11:50:00"], freq='min')
        >>> idx.minute
        Index([30, 50], dtype='int64')
        """,
    )
    second = _field_accessor(
        "second",
        """
        The second of the period.

        See Also
        --------
        PeriodIndex.hour : The hour of the period.
        PeriodIndex.minute : The minute of the period.
        PeriodIndex.to_timestamp : Cast to DatetimeArray/Index.

        Examples
        --------
        >>> idx = pd.PeriodIndex(["2023-01-01 10:00:30",
        ...                       "2023-01-01 10:00:31"], freq='s')
        >>> idx.second
        Index([30, 31], dtype='int64')
        """,
    )
    weekofyear = _field_accessor(
        "week",
        """
        The week ordinal of the year.

        See Also
        --------
        PeriodIndex.day_of_week : The day of the week with Monday=0, Sunday=6.
        PeriodIndex.dayofweek : The day of the week with Monday=0, Sunday=6.
        PeriodIndex.week : The week ordinal of the year.
        PeriodIndex.weekday : The day of the week with Monday=0, Sunday=6.
        PeriodIndex.year : The year of the period.

        Examples
        --------
        >>> idx = pd.PeriodIndex(["2023-01", "2023-02", "2023-03"], freq="M")
        >>> idx.week  # It can be written `weekofyear`
        Index([5, 9, 13], dtype='int64')
        """,
    )
    week = weekofyear
    day_of_week = _field_accessor(
        "day_of_week",
        """
        The day of the week with Monday=0, Sunday=6.

        See Also
        --------
        PeriodIndex.day : The days of the period.
        PeriodIndex.day_of_week : The day of the week with Monday=0, Sunday=6.
        PeriodIndex.day_of_year : The ordinal day of the year.
        PeriodIndex.dayofweek : The day of the week with Monday=0, Sunday=6.
        PeriodIndex.dayofyear : The ordinal day of the year.
        PeriodIndex.week : The week ordinal of the year.
        PeriodIndex.weekday : The day of the week with Monday=0, Sunday=6.
        PeriodIndex.weekofyear : The week ordinal of the year.

        Examples
        --------
        >>> idx = pd.PeriodIndex(["2023-01-01", "2023-01-02", "2023-01-03"], freq="D")
        >>> idx.weekday
        Index([6, 0, 1], dtype='int64')
        """,
    )
    dayofweek = day_of_week
    weekday = dayofweek
    dayofyear = day_of_year = _field_accessor(
        "day_of_year",
        """
        The ordinal day of the year.

        See Also
        --------
        PeriodIndex.day : The days of the period.
        PeriodIndex.day_of_week : The day of the week with Monday=0, Sunday=6.
        PeriodIndex.day_of_year : The ordinal day of the year.
        PeriodIndex.dayofweek : The day of the week with Monday=0, Sunday=6.
        PeriodIndex.dayofyear : The ordinal day of the year.
        PeriodIndex.weekday : The day of the week with Monday=0, Sunday=6.
        PeriodIndex.weekofyear : The week ordinal of the year.
        PeriodIndex.year : The year of the period.

        Examples
        --------
        >>> idx = pd.PeriodIndex(["2023-01-10", "2023-02-01", "2023-03-01"], freq="D")
        >>> idx.dayofyear
        Index([10, 32, 60], dtype='int64')

        >>> idx = pd.PeriodIndex(["2023", "2024", "2025"], freq="Y")
        >>> idx
        PeriodIndex(['2023', '2024', '2025'], dtype='period[Y-DEC]')
        >>> idx.dayofyear
        Index([365, 366, 365], dtype='int64')
        """,
    )
    quarter = _field_accessor(
        "quarter",
        """
        The quarter of the date.

        See Also
        --------
        PeriodIndex.qyear : Fiscal year the Period lies in according to its
            starting-quarter.

        Examples
        --------
        >>> idx = pd.PeriodIndex(["2023-01", "2023-02", "2023-03"], freq="M")
        >>> idx.quarter
        Index([1, 1, 1], dtype='int64')
        """,
    )
    qyear = _field_accessor(
        "qyear",
        """
        Fiscal year the Period lies in according to its starting-quarter.

        The `year` and the `qyear` of the period will be the same if the fiscal
        and calendar years are the same. When they are not, the fiscal year
        can be different from the calendar year of the period.

        Returns
        -------
        int
            The fiscal year of the period.

        See Also
        --------
        PeriodIndex.quarter : The quarter of the date.
        PeriodIndex.year : The year of the period.

        Examples
        --------
        If the natural and fiscal year are the same, `qyear` and `year` will
        be the same.

        >>> per = pd.Period('2018Q1', freq='Q')
        >>> per.qyear
        2018
        >>> per.year
        2018

        If the fiscal year starts in April (`Q-MAR`), the first quarter of
        2018 will start in April 2017. `year` will then be 2017, but `qyear`
        will be the fiscal year, 2018.

        >>> per = pd.Period('2018Q1', freq='Q-MAR')
        >>> per.start_time
        Timestamp('2017-04-01 00:00:00')
        >>> per.qyear
        2018
        >>> per.year
        2017
        """,
    )

    days_in_month = _field_accessor(
        "days_in_month",
        """
        The number of days in the month.

        See Also
        --------
        PeriodIndex.day : The days of the period.
        PeriodIndex.days_in_month : The number of days in the month.
        PeriodIndex.daysinmonth : The number of days in the month.
        PeriodIndex.month : The month as January=1, December=12.

        Examples
        --------
        For Series:

        >>> period = pd.period_range('2020-1-1 00:00', '2020-3-1 00:00', freq='M')
        >>> s = pd.Series(period)
        >>> s
        0   2020-01
        1   2020-02
        2   2020-03
        dtype: period[M]
        >>> s.dt.days_in_month
        0    31
        1    29
        2    31
        dtype: int64

        For PeriodIndex:

        >>> idx = pd.PeriodIndex(["2023-01", "2023-02", "2023-03"], freq="M")
        >>> idx.days_in_month   # It can be also entered as `daysinmonth`
        Index([31, 28, 31], dtype='int64')
        """,
    )
    daysinmonth = days_in_month

    @property
    def is_leap_year(self) -> npt.NDArray[np.bool_]:
        """
        Logical indicating if the date belongs to a leap year.

        See Also
        --------
        PeriodIndex.qyear : Fiscal year the Period lies in according to its
            starting-quarter.
        PeriodIndex.year : The year of the period.

        Examples
        --------
        >>> idx = pd.PeriodIndex(["2023", "2024", "2025"], freq="Y")
        >>> idx.is_leap_year
        array([False,  True, False])
        """
        return isleapyear_arr(np.asarray(self.year))

    def to_timestamp(self, freq=None, how: str = "start") -> DatetimeArray:
        """
        Cast to DatetimeArray/Index.

        Parameters
        ----------
        freq : str or DateOffset, optional
            Target frequency. The default is 'D' for week or longer,
            's' otherwise.
        how : {'s', 'e', 'start', 'end'}
            Whether to use the start or end of the time period being converted.

        Returns
        -------
        DatetimeArray/Index
            Timestamp representation of given Period-like object.

        See Also
        --------
        PeriodIndex.day : The days of the period.
        PeriodIndex.from_fields : Construct a PeriodIndex from fields
            (year, month, day, etc.).
        PeriodIndex.from_ordinals : Construct a PeriodIndex from ordinals.
        PeriodIndex.hour : The hour of the period.
        PeriodIndex.minute : The minute of the period.
        PeriodIndex.month : The month as January=1, December=12.
        PeriodIndex.second : The second of the period.
        PeriodIndex.year : The year of the period.

        Examples
        --------
        >>> idx = pd.PeriodIndex(["2023-01", "2023-02", "2023-03"], freq="M")
        >>> idx.to_timestamp()
        DatetimeIndex(['2023-01-01', '2023-02-01', '2023-03-01'],
        dtype='datetime64[ns]', freq='MS')

        The frequency will not be inferred if the index contains less than
        three elements, or if the values of index are not strictly monotonic:

        >>> idx = pd.PeriodIndex(["2023-01", "2023-02"], freq="M")
        >>> idx.to_timestamp()
        DatetimeIndex(['2023-01-01', '2023-02-01'], dtype='datetime64[ns]', freq=None)

        >>> idx = pd.PeriodIndex(
        ...     ["2023-01", "2023-02", "2023-02", "2023-03"], freq="2M"
        ... )
        >>> idx.to_timestamp()
        DatetimeIndex(['2023-01-01', '2023-02-01', '2023-02-01', '2023-03-01'],
        dtype='datetime64[ns]', freq=None)
        """
        from pandas.core.arrays import DatetimeArray

        how = libperiod.validate_end_alias(how)

        end = how == "E"
        if end:
            if freq == "B" or self.freq == "B":
                # roll forward to ensure we land on B date
                adjust = Timedelta(1, "D") - Timedelta(1, "ns")
                return self.to_timestamp(how="start") + adjust
            else:
                adjust = Timedelta(1, "ns")
                return (self + self.freq).to_timestamp(how="start") - adjust

        if freq is None:
            freq_code = self._dtype._get_to_timestamp_base()
            dtype = PeriodDtypeBase(freq_code, 1)
            freq = dtype._freqstr
            base = freq_code
        else:
            freq = Period._maybe_convert_freq(freq)
            base = freq._period_dtype_code

        new_parr = self.asfreq(freq, how=how)

        new_data = libperiod.periodarr_to_dt64arr(new_parr.asi8, base)
        dta = DatetimeArray._from_sequence(new_data, dtype=np.dtype("M8[ns]"))

        if self.freq.name == "B":
            # See if we can retain BDay instead of Day in cases where
            #  len(self) is too small for infer_freq to distinguish between them
            diffs = libalgos.unique_deltas(self.asi8)
            if len(diffs) == 1:
                diff = diffs[0]
                if diff == self.dtype._n:
                    dta._freq = self.freq
                elif diff == 1:
                    dta._freq = self.freq.base
                # TODO: other cases?
            return dta
        else:
            return dta._with_freq("infer")

    # --------------------------------------------------------------------

    def _box_func(self, x) -> Period | NaTType:
        return Period._from_ordinal(ordinal=x, freq=self.freq)

    @doc(**_shared_doc_kwargs, other="PeriodIndex", other_name="PeriodIndex")
    def asfreq(self, freq=None, how: str = "E") -> Self:
        """
        Convert the {klass} to the specified frequency `freq`.

        Equivalent to applying :meth:`pandas.Period.asfreq` with the given arguments
        to each :class:`~pandas.Period` in this {klass}.

        Parameters
        ----------
        freq : str
            A frequency.
        how : str {{'E', 'S'}}, default 'E'
            Whether the elements should be aligned to the end
            or start within pa period.

            * 'E', 'END', or 'FINISH' for end,
            * 'S', 'START', or 'BEGIN' for start.

            January 31st ('END') vs. January 1st ('START') for example.

        Returns
        -------
        {klass}
            The transformed {klass} with the new frequency.

        See Also
        --------
        {other}.asfreq: Convert each Period in a {other_name} to the given frequency.
        Period.asfreq : Convert a :class:`~pandas.Period` object to the given frequency.

        Examples
        --------
        >>> pidx = pd.period_range("2010-01-01", "2015-01-01", freq="Y")
        >>> pidx
        PeriodIndex(['2010', '2011', '2012', '2013', '2014', '2015'],
        dtype='period[Y-DEC]')

        >>> pidx.asfreq("M")
        PeriodIndex(['2010-12', '2011-12', '2012-12', '2013-12', '2014-12',
        '2015-12'], dtype='period[M]')

        >>> pidx.asfreq("M", how="S")
        PeriodIndex(['2010-01', '2011-01', '2012-01', '2013-01', '2014-01',
        '2015-01'], dtype='period[M]')
        """
        how = libperiod.validate_end_alias(how)
        if isinstance(freq, BaseOffset) and hasattr(freq, "_period_dtype_code"):
            freq = PeriodDtype(freq)._freqstr
        freq = Period._maybe_convert_freq(freq)

        base1 = self._dtype._dtype_code
        base2 = freq._period_dtype_code

        asi8 = self.asi8
        # self.freq.n can't be negative or 0
        end = how == "E"
        if end:
            ordinal = asi8 + self.dtype._n - 1
        else:
            ordinal = asi8

        new_data = period_asfreq_arr(ordinal, base1, base2, end)

        if self._hasna:
            new_data[self._isnan] = iNaT

        dtype = PeriodDtype(freq)
        return type(self)(new_data, dtype=dtype)

    # ------------------------------------------------------------------
    # Rendering Methods

    def _formatter(self, boxed: bool = False) -> Callable[[object], str]:
        if boxed:
            return str
        return "'{}'".format

    def _format_native_types(
        self, *, na_rep: str | float = "NaT", date_format=None, **kwargs
    ) -> npt.NDArray[np.object_]:
        """
        actually format my specific types
        """
        return libperiod.period_array_strftime(
            self.asi8, self.dtype._dtype_code, na_rep, date_format
        )

    # ------------------------------------------------------------------

    def astype(self, dtype, copy: bool = True):
        # We handle Period[T] -> Period[U]
        # Our parent handles everything else.
        dtype = pandas_dtype(dtype)
        if dtype == self._dtype:
            if not copy:
                return self
            else:
                return self.copy()
        if isinstance(dtype, PeriodDtype):
            return self.asfreq(dtype.freq)

        if lib.is_np_dtype(dtype, "M") or isinstance(dtype, DatetimeTZDtype):
            # GH#45038 match PeriodIndex behavior.
            tz = getattr(dtype, "tz", None)
            unit = dtl.dtype_to_unit(dtype)
            return self.to_timestamp().tz_localize(tz).as_unit(unit)

        return super().astype(dtype, copy=copy)

    def searchsorted(
        self,
        value: NumpyValueArrayLike | ExtensionArray,
        side: Literal["left", "right"] = "left",
        sorter: NumpySorter | None = None,
    ) -> npt.NDArray[np.intp] | np.intp:
        npvalue = self._validate_setitem_value(value).view("M8[ns]")

        # Cast to M8 to get datetime-like NaT placement,
        #  similar to dtl._period_dispatch
        m8arr = self._ndarray.view("M8[ns]")
        return m8arr.searchsorted(npvalue, side=side, sorter=sorter)

    def _pad_or_backfill(
        self,
        *,
        method: FillnaOptions,
        limit: int | None = None,
        limit_area: Literal["inside", "outside"] | None = None,
        copy: bool = True,
    ) -> Self:
        # view as dt64 so we get treated as timelike in core.missing,
        #  similar to dtl._period_dispatch
        dta = self.view("M8[ns]")
        result = dta._pad_or_backfill(
            method=method, limit=limit, limit_area=limit_area, copy=copy
        )
        if copy:
            return cast("Self", result.view(self.dtype))
        else:
            return self

    # ------------------------------------------------------------------
    # Arithmetic Methods

    def _addsub_int_array_or_scalar(
        self, other: np.ndarray | int, op: Callable[[Any, Any], Any]
    ) -> Self:
        """
        Add or subtract array of integers.

        Parameters
        ----------
        other : np.ndarray[int64] or int
        op : {operator.add, operator.sub}

        Returns
        -------
        result : PeriodArray
        """
        assert op in [operator.add, operator.sub]
        if op is operator.sub:
            other = -other
        res_values = add_overflowsafe(self.asi8, np.asarray(other, dtype="i8"))
        return type(self)(res_values, dtype=self.dtype)

    def _add_offset(self, other: BaseOffset):
        assert not isinstance(other, Tick)

        self._require_matching_freq(other, base=True)
        return self._addsub_int_array_or_scalar(other.n, operator.add)

    # TODO: can we de-duplicate with Period._add_timedeltalike_scalar?
    def _add_timedeltalike_scalar(self, other):
        """
        Parameters
        ----------
        other : timedelta, Tick, np.timedelta64

        Returns
        -------
        PeriodArray
        """
        if not isinstance(self.freq, Tick):
            # We cannot add timedelta-like to non-tick PeriodArray
            raise raise_on_incompatible(self, other)

        if isna(other):
            # i.e. np.timedelta64("NaT")
            return super()._add_timedeltalike_scalar(other)

        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

        res_values = add_overflowsafe(self.asi8, np.asarray(delta.view("i8")))
        return type(self)(res_values, dtype=self.dtype)

    def _check_timedeltalike_freq_compat(self, other):
        """
        Arithmetic operations with timedelta-like scalars or array `other`
        are only valid if `other` is an integer multiple of `self.freq`.
        If the operation is valid, find that integer multiple.  Otherwise,
        raise because the operation is invalid.

        Parameters
        ----------
        other : timedelta, np.timedelta64, Tick,
                ndarray[timedelta64], TimedeltaArray, TimedeltaIndex

        Returns
        -------
        multiple : int or ndarray[int64]

        Raises
        ------
        IncompatibleFrequency
        """
        assert self.dtype._is_tick_like()  # checked by calling function

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

        if isinstance(other, (timedelta, np.timedelta64, Tick)):
            td = np.asarray(Timedelta(other).asm8)
        else:
            td = np.asarray(other)

        try:
            delta = astype_overflowsafe(td, dtype=dtype, copy=False, round_ok=False)
        except ValueError as err:
            raise raise_on_incompatible(self, other) from err

        delta = delta.view("i8")
        return lib.item_from_zerodim(delta)

    # ------------------------------------------------------------------
    # Reductions

    def _reduce(
        self, name: str, *, skipna: bool = True, keepdims: bool = False, **kwargs
    ):
        result = super()._reduce(name, skipna=skipna, keepdims=keepdims, **kwargs)
        if keepdims and isinstance(result, np.ndarray):
            return self._from_sequence(result, dtype=self.dtype)
        return result


def raise_on_incompatible(left, right) -> IncompatibleFrequency:
    """
    Helper function to render a consistent error message when raising
    IncompatibleFrequency.

    Parameters
    ----------
    left : PeriodArray
    right : None, DateOffset, Period, ndarray, or timedelta-like

    Returns
    -------
    IncompatibleFrequency
        Exception to be raised by the caller.
    """
    # GH#24283 error message format depends on whether right is scalar
    if isinstance(right, (np.ndarray, ABCTimedeltaArray)) or right is None:
        other_freq = None
    elif isinstance(right, BaseOffset):
        with warnings.catch_warnings():
            warnings.filterwarnings(
                "ignore", r"PeriodDtype\[B\] is deprecated", category=FutureWarning
            )
            other_freq = PeriodDtype(right)._freqstr
    elif isinstance(right, (ABCPeriodIndex, PeriodArray, Period)):
        other_freq = right.freqstr
    else:
        other_freq = delta_to_tick(Timedelta(right)).freqstr

    own_freq = PeriodDtype(left.freq)._freqstr
    msg = DIFFERENT_FREQ.format(
        cls=type(left).__name__, own_freq=own_freq, other_freq=other_freq
    )
    return IncompatibleFrequency(msg)


# -------------------------------------------------------------------
# Constructor Helpers


def period_array(
    data: Sequence[Period | str | None] | AnyArrayLike,
    freq: str | Tick | BaseOffset | None = None,
    copy: bool = False,
) -> PeriodArray:
    """
    Construct a new PeriodArray from a sequence of Period scalars.

    Parameters
    ----------
    data : Sequence of Period objects
        A sequence of Period objects. These are required to all have
        the same ``freq.`` Missing values can be indicated by ``None``
        or ``pandas.NaT``.
    freq : str, Tick, or Offset
        The frequency of every element of the array. This can be specified
        to avoid inferring the `freq` from `data`.
    copy : bool, default False
        Whether to ensure a copy of the data is made.

    Returns
    -------
    PeriodArray

    See Also
    --------
    PeriodArray
    pandas.PeriodIndex

    Examples
    --------
    >>> period_array([pd.Period("2017", freq="Y"), pd.Period("2018", freq="Y")])
    <PeriodArray>
    ['2017', '2018']
    Length: 2, dtype: period[Y-DEC]

    >>> period_array([pd.Period("2017", freq="Y"), pd.Period("2018", freq="Y"), pd.NaT])
    <PeriodArray>
    ['2017', '2018', 'NaT']
    Length: 3, dtype: period[Y-DEC]

    Integers that look like years are handled

    >>> period_array([2000, 2001, 2002], freq="D")
    <PeriodArray>
    ['2000-01-01', '2001-01-01', '2002-01-01']
    Length: 3, dtype: period[D]

    Datetime-like strings may also be passed

    >>> period_array(["2000-Q1", "2000-Q2", "2000-Q3", "2000-Q4"], freq="Q")
    <PeriodArray>
    ['2000Q1', '2000Q2', '2000Q3', '2000Q4']
    Length: 4, dtype: period[Q-DEC]
    """
    data_dtype = getattr(data, "dtype", None)

    if lib.is_np_dtype(data_dtype, "M"):
        return PeriodArray._from_datetime64(data, freq)
    if isinstance(data_dtype, PeriodDtype):
        out = PeriodArray(data)
        if freq is not None:
            if freq == data_dtype.freq:
                return out
            return out.asfreq(freq)
        return out

    # other iterable of some kind
    if not isinstance(data, (np.ndarray, list, tuple, ABCSeries)):
        data = list(data)

    arrdata = np.asarray(data)

    dtype: PeriodDtype | None
    if freq:
        dtype = PeriodDtype(freq)
    else:
        dtype = None

    if arrdata.dtype.kind == "f" and len(arrdata) > 0:
        raise TypeError("PeriodIndex does not allow floating point in construction")

    if arrdata.dtype.kind in "iu":
        arr = arrdata.astype(np.int64, copy=False)
        # error: Argument 2 to "from_ordinals" has incompatible type "Union[str,
        # Tick, None]"; expected "Union[timedelta, BaseOffset, str]"
        ordinals = libperiod.from_ordinals(arr, freq)  # type: ignore[arg-type]
        return PeriodArray(ordinals, dtype=dtype)

    data = ensure_object(arrdata)
    if freq is None:
        freq = libperiod.extract_freq(data)
    dtype = PeriodDtype(freq)
    return PeriodArray._from_sequence(data, dtype=dtype)


@overload
def validate_dtype_freq(dtype, freq: BaseOffsetT) -> BaseOffsetT: ...


@overload
def validate_dtype_freq(dtype, freq: timedelta | str | None) -> BaseOffset: ...


def validate_dtype_freq(
    dtype, freq: BaseOffsetT | BaseOffset | timedelta | str | None
) -> BaseOffsetT:
    """
    If both a dtype and a freq are available, ensure they match.  If only
    dtype is available, extract the implied freq.

    Parameters
    ----------
    dtype : dtype
    freq : DateOffset or None

    Returns
    -------
    freq : DateOffset

    Raises
    ------
    ValueError : non-period dtype
    IncompatibleFrequency : mismatch between dtype and freq
    """
    if freq is not None:
        freq = to_offset(freq, is_period=True)

    if dtype is not None:
        dtype = pandas_dtype(dtype)
        if not isinstance(dtype, PeriodDtype):
            raise ValueError("dtype must be PeriodDtype")
        if freq is None:
            freq = dtype.freq
        elif freq != dtype.freq:
            raise IncompatibleFrequency("specified freq and dtype are different")
    # error: Incompatible return value type (got "Union[BaseOffset, Any, None]",
    # expected "BaseOffset")
    return freq  # type: ignore[return-value]


def dt64arr_to_periodarr(
    data, freq, tz=None
) -> tuple[npt.NDArray[np.int64], BaseOffset]:
    """
    Convert an datetime-like array to values Period ordinals.

    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):
            data, freq = data._values, data.dt.freq

    elif isinstance(data, (ABCIndex, ABCSeries)):
        data = data._values

    reso = get_unit_from_dtype(data.dtype)
    freq = Period._maybe_convert_freq(freq)
    base = freq._period_dtype_code
    return c_dt64arr_to_periodarr(data.view("i8"), base, tz, reso=reso), freq


def _get_ordinal_range(start, end, periods, freq, mult: int = 1):
    if com.count_not_none(start, end, periods) != 2:
        raise ValueError(
            "Of the three parameters: start, end, and periods, "
            "exactly two must be specified"
        )

    if freq is not None:
        freq = to_offset(freq, is_period=True)
        mult = freq.n

    if start is not None:
        start = Period(start, freq)
    if end is not None:
        end = Period(end, freq)

    is_start_per = isinstance(start, Period)
    is_end_per = isinstance(end, Period)

    if is_start_per and is_end_per and start.freq != end.freq:
        raise ValueError("start and end must have same freq")
    if start is NaT or end is NaT:
        raise ValueError("start and end must not be NaT")

    if freq is None:
        if is_start_per:
            freq = start.freq
        elif is_end_per:
            freq = end.freq
        else:  # pragma: no cover
            raise ValueError("Could not infer freq from start/end")
        mult = freq.n

    if periods is not None:
        periods = periods * mult
        if start is None:
            data = np.arange(
                end.ordinal - periods + mult, end.ordinal + 1, mult, dtype=np.int64
            )
        else:
            data = np.arange(
                start.ordinal, start.ordinal + periods, mult, dtype=np.int64
            )
    else:
        data = np.arange(start.ordinal, end.ordinal + 1, mult, dtype=np.int64)

    return data, freq


def _range_from_fields(
    year=None,
    month=None,
    quarter=None,
    day=None,
    hour=None,
    minute=None,
    second=None,
    freq=None,
) -> tuple[np.ndarray, BaseOffset]:
    if hour is None:
        hour = 0
    if minute is None:
        minute = 0
    if second is None:
        second = 0
    if day is None:
        day = 1

    ordinals = []

    if quarter is not None:
        if freq is None:
            freq = to_offset("Q", is_period=True)
            base = FreqGroup.FR_QTR.value
        else:
            freq = to_offset(freq, is_period=True)
            base = libperiod.freq_to_dtype_code(freq)
            if base != FreqGroup.FR_QTR.value:
                raise AssertionError("base must equal FR_QTR")

        freqstr = freq.freqstr
        year, quarter = _make_field_arrays(year, quarter)
        for y, q in zip(year, quarter):
            calendar_year, calendar_month = parsing.quarter_to_myear(y, q, freqstr)
            val = libperiod.period_ordinal(
                calendar_year, calendar_month, 1, 1, 1, 1, 0, 0, base
            )
            ordinals.append(val)
    else:
        freq = to_offset(freq, is_period=True)
        base = libperiod.freq_to_dtype_code(freq)
        arrays = _make_field_arrays(year, month, day, hour, minute, second)
        for y, mth, d, h, mn, s in zip(*arrays):
            ordinals.append(libperiod.period_ordinal(y, mth, d, h, mn, s, 0, 0, base))

    return np.array(ordinals, dtype=np.int64), freq


def _make_field_arrays(*fields) -> list[np.ndarray]:
    length = None
    for x in fields:
        if isinstance(x, (list, np.ndarray, ABCSeries)):
            if length is not None and len(x) != length:
                raise ValueError("Mismatched Period array lengths")
            if length is None:
                length = len(x)

    # error: Argument 2 to "repeat" has incompatible type "Optional[int]"; expected
    # "Union[Union[int, integer[Any]], Union[bool, bool_], ndarray, Sequence[Union[int,
    # integer[Any]]], Sequence[Union[bool, bool_]], Sequence[Sequence[Any]]]"
    return [
        np.asarray(x)
        if isinstance(x, (np.ndarray, list, ABCSeries))
        else np.repeat(x, length)  # type: ignore[arg-type]
        for x in fields
    ]