from collections.abc import Sequence from typing import overload import numpy as np from pandas.core.arrays.base import ( ExtensionArray, ExtensionOpsMixin, ) from typing_extensions import ( Self, TypeAlias, ) from pandas._libs import ( NaT as NaT, NaTType as NaTType, ) from pandas._typing import ( DatetimeLikeScalar, PositionalIndexerTuple, ScalarIndexer, SequenceIndexer, TimeAmbiguous, TimeNonexistent, TimeUnit, ) DTScalarOrNaT: TypeAlias = DatetimeLikeScalar | NaTType class DatelikeOps: def strftime(self, date_format): ... class TimelikeOps: @property def unit(self) -> TimeUnit: ... def as_unit(self, unit: TimeUnit) -> Self: ... def round( self, freq, ambiguous: TimeAmbiguous = "raise", nonexistent: TimeNonexistent = "raise", ): """ Perform round operation on the data to the specified `freq`. Parameters ---------- freq : str or Offset The frequency level to round the index to. Must be a fixed frequency like 'S' (second) not 'ME' (month end). See :ref:`frequency aliases ` for a list of possible `freq` values. ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise' Only relevant for DatetimeIndex: - 'infer' will attempt to infer fall dst-transition hours based on order - bool-ndarray where True signifies a DST time, False designates a non-DST time (note that this flag is only applicable for ambiguous times) - 'NaT' will return NaT where there are ambiguous times - 'raise' will raise an AmbiguousTimeError if there are ambiguous times. nonexistent : 'shift_forward', 'shift_backward', 'NaT', timedelta, default 'raise' A nonexistent time does not exist in a particular timezone where clocks moved forward due to DST. - 'shift_forward' will shift the nonexistent time forward to the closest existing time - 'shift_backward' will shift the nonexistent time backward to the closest existing time - 'NaT' will return NaT where there are nonexistent times - timedelta objects will shift nonexistent times by the timedelta - 'raise' will raise an NonExistentTimeError if there are nonexistent times. Returns ------- DatetimeIndex, TimedeltaIndex, or Series Index of the same type for a DatetimeIndex or TimedeltaIndex, or a Series with the same index for a Series. Raises ------ ValueError if the `freq` cannot be converted. Notes ----- If the timestamps have a timezone, rounding will take place relative to the local ("wall") time and re-localized to the same timezone. When rounding near daylight savings time, use ``nonexistent`` and ``ambiguous`` to control the re-localization behavior. Examples -------- **DatetimeIndex** >>> rng = pd.date_range('1/1/2018 11:59:00', periods=3, freq='min') >>> rng DatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 12:00:00', '2018-01-01 12:01:00'], dtype='datetime64[ns]', freq='min') >>> rng.round('h') DatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00', '2018-01-01 12:00:00'], dtype='datetime64[ns]', freq=None) **Series** >>> pd.Series(rng).dt.round("h") 0 2018-01-01 12:00:00 1 2018-01-01 12:00:00 2 2018-01-01 12:00:00 dtype: datetime64[ns] When rounding near a daylight savings time transition, use ``ambiguous`` or ``nonexistent`` to control how the timestamp should be re-localized. >>> rng_tz = pd.DatetimeIndex(["2021-10-31 03:30:00"], tz="Europe/Amsterdam") >>> rng_tz.floor("2h", ambiguous=False) DatetimeIndex(['2021-10-31 02:00:00+01:00'], dtype='datetime64[ns, Europe/Amsterdam]', freq=None) >>> rng_tz.floor("2h", ambiguous=True) DatetimeIndex(['2021-10-31 02:00:00+02:00'], dtype='datetime64[ns, Europe/Amsterdam]', freq=None) """ pass def floor( self, freq, ambiguous: TimeAmbiguous = "raise", nonexistent: TimeNonexistent = "raise", ): """ Perform floor operation on the data to the specified `freq`. Parameters ---------- freq : str or Offset The frequency level to floor the index to. Must be a fixed frequency like 'S' (second) not 'ME' (month end). See :ref:`frequency aliases ` for a list of possible `freq` values. ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise' Only relevant for DatetimeIndex: - 'infer' will attempt to infer fall dst-transition hours based on order - bool-ndarray where True signifies a DST time, False designates a non-DST time (note that this flag is only applicable for ambiguous times) - 'NaT' will return NaT where there are ambiguous times - 'raise' will raise an AmbiguousTimeError if there are ambiguous times. nonexistent : 'shift_forward', 'shift_backward', 'NaT', timedelta, default 'raise' A nonexistent time does not exist in a particular timezone where clocks moved forward due to DST. - 'shift_forward' will shift the nonexistent time forward to the closest existing time - 'shift_backward' will shift the nonexistent time backward to the closest existing time - 'NaT' will return NaT where there are nonexistent times - timedelta objects will shift nonexistent times by the timedelta - 'raise' will raise an NonExistentTimeError if there are nonexistent times. Returns ------- DatetimeIndex, TimedeltaIndex, or Series Index of the same type for a DatetimeIndex or TimedeltaIndex, or a Series with the same index for a Series. Raises ------ ValueError if the `freq` cannot be converted. Notes ----- If the timestamps have a timezone, flooring will take place relative to the local ("wall") time and re-localized to the same timezone. When flooring near daylight savings time, use ``nonexistent`` and ``ambiguous`` to control the re-localization behavior. Examples -------- **DatetimeIndex** >>> rng = pd.date_range('1/1/2018 11:59:00', periods=3, freq='min') >>> rng DatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 12:00:00', '2018-01-01 12:01:00'], dtype='datetime64[ns]', freq='min') >>> rng.floor('h') DatetimeIndex(['2018-01-01 11:00:00', '2018-01-01 12:00:00', '2018-01-01 12:00:00'], dtype='datetime64[ns]', freq=None) **Series** >>> pd.Series(rng).dt.floor("h") 0 2018-01-01 11:00:00 1 2018-01-01 12:00:00 2 2018-01-01 12:00:00 dtype: datetime64[ns] When rounding near a daylight savings time transition, use ``ambiguous`` or ``nonexistent`` to control how the timestamp should be re-localized. >>> rng_tz = pd.DatetimeIndex(["2021-10-31 03:30:00"], tz="Europe/Amsterdam") >>> rng_tz.floor("2h", ambiguous=False) DatetimeIndex(['2021-10-31 02:00:00+01:00'], dtype='datetime64[ns, Europe/Amsterdam]', freq=None) >>> rng_tz.floor("2h", ambiguous=True) DatetimeIndex(['2021-10-31 02:00:00+02:00'], dtype='datetime64[ns, Europe/Amsterdam]', freq=None) """ pass def ceil( self, freq, ambiguous: TimeAmbiguous = "raise", nonexistent: TimeNonexistent = "raise", ): """ Perform ceil operation on the data to the specified `freq`. Parameters ---------- freq : str or Offset The frequency level to ceil the index to. Must be a fixed frequency like 'S' (second) not 'ME' (month end). See :ref:`frequency aliases ` for a list of possible `freq` values. ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise' Only relevant for DatetimeIndex: - 'infer' will attempt to infer fall dst-transition hours based on order - bool-ndarray where True signifies a DST time, False designates a non-DST time (note that this flag is only applicable for ambiguous times) - 'NaT' will return NaT where there are ambiguous times - 'raise' will raise an AmbiguousTimeError if there are ambiguous times. nonexistent : 'shift_forward', 'shift_backward', 'NaT', timedelta, default 'raise' A nonexistent time does not exist in a particular timezone where clocks moved forward due to DST. - 'shift_forward' will shift the nonexistent time forward to the closest existing time - 'shift_backward' will shift the nonexistent time backward to the closest existing time - 'NaT' will return NaT where there are nonexistent times - timedelta objects will shift nonexistent times by the timedelta - 'raise' will raise an NonExistentTimeError if there are nonexistent times. Returns ------- DatetimeIndex, TimedeltaIndex, or Series Index of the same type for a DatetimeIndex or TimedeltaIndex, or a Series with the same index for a Series. Raises ------ ValueError if the `freq` cannot be converted. Notes ----- If the timestamps have a timezone, ceiling will take place relative to the local ("wall") time and re-localized to the same timezone. When ceiling near daylight savings time, use ``nonexistent`` and ``ambiguous`` to control the re-localization behavior. Examples -------- **DatetimeIndex** >>> rng = pd.date_range('1/1/2018 11:59:00', periods=3, freq='min') >>> rng DatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 12:00:00', '2018-01-01 12:01:00'], dtype='datetime64[ns]', freq='min') >>> rng.ceil('h') DatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00', '2018-01-01 13:00:00'], dtype='datetime64[ns]', freq=None) **Series** >>> pd.Series(rng).dt.ceil("h") 0 2018-01-01 12:00:00 1 2018-01-01 12:00:00 2 2018-01-01 13:00:00 dtype: datetime64[ns] When rounding near a daylight savings time transition, use ``ambiguous`` or ``nonexistent`` to control how the timestamp should be re-localized. >>> rng_tz = pd.DatetimeIndex(["2021-10-31 01:30:00"], tz="Europe/Amsterdam") >>> rng_tz.ceil("h", ambiguous=False) DatetimeIndex(['2021-10-31 02:00:00+01:00'], dtype='datetime64[ns, Europe/Amsterdam]', freq=None) >>> rng_tz.ceil("h", ambiguous=True) DatetimeIndex(['2021-10-31 02:00:00+02:00'], dtype='datetime64[ns, Europe/Amsterdam]', freq=None) """ pass class DatetimeLikeArrayMixin(ExtensionOpsMixin, ExtensionArray): @property def ndim(self) -> int: ... @property def shape(self): ... def reshape(self, *args, **kwargs): ... def ravel(self, *args, **kwargs): ... # pyrefly: ignore def __iter__(self): ... @property def asi8(self) -> np.ndarray: ... @property def nbytes(self): ... def __array__(self, dtype=...) -> np.ndarray: ... @property def size(self) -> int: ... def __len__(self) -> int: ... @overload def __getitem__(self, key: ScalarIndexer) -> DTScalarOrNaT: ... @overload def __getitem__( self, key: SequenceIndexer | PositionalIndexerTuple, ) -> Self: ... def __setitem__( # type: ignore[override] # pyright: ignore[reportIncompatibleMethodOverride] self, key: int | Sequence[int] | Sequence[bool] | slice, value ) -> None: ... def astype(self, dtype, copy: bool = True): ... def view(self, dtype=...): ... def unique(self): ... def copy(self): ... def shift(self, periods: int = 1, fill_value=..., axis: int = ...): ... def searchsorted(self, value, side: str = ..., sorter=...): ... def repeat(self, repeats, *args, **kwargs): ... # pyrefly: ignore def value_counts(self, dropna: bool = True): ... def map(self, mapper): ... def isna(self): ... def fillna(self, value=..., method=None, limit=None): ... @property def freq(self): ... @freq.setter def freq(self, value) -> None: ... @property def freqstr(self): ... @property def inferred_freq(self): ... @property def resolution(self): ... __pow__ = ... __rpow__ = ... __rmul__ = ... def __add__(self, other): ... def __radd__(self, other): ... def __sub__(self, other): ... def __rsub__(self, other): ... def __iadd__(self, other): ... def __isub__(self, other): ... def min(self, *, axis=..., skipna: bool = ..., **kwargs): ... def max(self, *, axis=..., skipna: bool = ..., **kwargs): ... def mean(self, *, skipna: bool = ...): ...