from __future__ import annotations from datetime import ( datetime, timedelta, tzinfo, ) from typing import ( TYPE_CHECKING, cast, overload, ) import warnings import numpy as np from pandas._libs import ( lib, tslib, ) from pandas._libs.tslibs import ( BaseOffset, NaT, NaTType, Resolution, Timestamp, astype_overflowsafe, fields, get_resolution, get_supported_dtype, get_unit_from_dtype, ints_to_pydatetime, is_date_array_normalized, is_supported_dtype, is_unitless, normalize_i8_timestamps, timezones, to_offset, tz_convert_from_utc, tzconversion, ) from pandas._libs.tslibs.dtypes import abbrev_to_npy_unit from pandas.errors import PerformanceWarning from pandas.util._exceptions import find_stack_level from pandas.util._validators import validate_inclusive from pandas.core.dtypes.common import ( DT64NS_DTYPE, INT64_DTYPE, is_bool_dtype, is_float_dtype, is_string_dtype, pandas_dtype, ) from pandas.core.dtypes.dtypes import ( DatetimeTZDtype, ExtensionDtype, PeriodDtype, ) from pandas.core.dtypes.missing import isna from pandas.core.arrays import datetimelike as dtl from pandas.core.arrays._ranges import generate_regular_range import pandas.core.common as com from pandas.tseries.frequencies import get_period_alias from pandas.tseries.offsets import ( Day, Tick, ) if TYPE_CHECKING: from collections.abc import Iterator from pandas._typing import ( ArrayLike, DateTimeErrorChoices, DtypeObj, IntervalClosedType, Self, TimeAmbiguous, TimeNonexistent, npt, ) from pandas import DataFrame from pandas.core.arrays import PeriodArray _ITER_CHUNKSIZE = 10_000 @overload def tz_to_dtype(tz: tzinfo, unit: str = ...) -> DatetimeTZDtype: ... @overload def tz_to_dtype(tz: None, unit: str = ...) -> np.dtype[np.datetime64]: ... def tz_to_dtype( tz: tzinfo | None, unit: str = "ns" ) -> np.dtype[np.datetime64] | DatetimeTZDtype: """ Return a datetime64[ns] dtype appropriate for the given timezone. Parameters ---------- tz : tzinfo or None unit : str, default "ns" Returns ------- np.dtype or Datetime64TZDType """ if tz is None: return np.dtype(f"M8[{unit}]") else: return DatetimeTZDtype(tz=tz, unit=unit) def _field_accessor(name: str, field: str, docstring: str | None = None): def f(self): values = self._local_timestamps() if field in self._bool_ops: result: np.ndarray if field.endswith(("start", "end")): freq = self.freq month_kw = 12 if freq: kwds = freq.kwds month_kw = kwds.get("startingMonth", kwds.get("month", 12)) result = fields.get_start_end_field( values, field, self.freqstr, month_kw, reso=self._creso ) else: result = fields.get_date_field(values, field, reso=self._creso) # these return a boolean by-definition return result if field in self._object_ops: result = fields.get_date_name_field(values, field, reso=self._creso) result = self._maybe_mask_results(result, fill_value=None) else: result = fields.get_date_field(values, field, reso=self._creso) result = self._maybe_mask_results( result, fill_value=None, convert="float64" ) 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 DatetimeArray(dtl.TimelikeOps, dtl.DatelikeOps): # type: ignore[misc] """ Pandas ExtensionArray for tz-naive or tz-aware datetime data. .. warning:: DatetimeArray is currently experimental, and its API may change without warning. In particular, :attr:`DatetimeArray.dtype` is expected to change to always be an instance of an ``ExtensionDtype`` subclass. Parameters ---------- values : Series, Index, DatetimeArray, ndarray The datetime data. For DatetimeArray `values` (or a Series or Index boxing one), `dtype` and `freq` will be extracted from `values`. dtype : numpy.dtype or DatetimeTZDtype Note that the only NumPy dtype allowed is 'datetime64[ns]'. freq : str or Offset, optional The frequency. copy : bool, default False Whether to copy the underlying array of values. Attributes ---------- None Methods ------- None Examples -------- >>> pd.arrays.DatetimeArray._from_sequence( ... pd.DatetimeIndex(['2023-01-01', '2023-01-02'], freq='D')) ['2023-01-01 00:00:00', '2023-01-02 00:00:00'] Length: 2, dtype: datetime64[ns] """ _typ = "datetimearray" _internal_fill_value = np.datetime64("NaT", "ns") _recognized_scalars = (datetime, np.datetime64) _is_recognized_dtype = lambda x: lib.is_np_dtype(x, "M") or isinstance( x, DatetimeTZDtype ) _infer_matches = ("datetime", "datetime64", "date") @property def _scalar_type(self) -> type[Timestamp]: return Timestamp # define my properties & methods for delegation _bool_ops: list[str] = [ "is_month_start", "is_month_end", "is_quarter_start", "is_quarter_end", "is_year_start", "is_year_end", "is_leap_year", ] _object_ops: list[str] = ["freq", "tz"] _field_ops: list[str] = [ "year", "month", "day", "hour", "minute", "second", "weekday", "dayofweek", "day_of_week", "dayofyear", "day_of_year", "quarter", "days_in_month", "daysinmonth", "microsecond", "nanosecond", ] _other_ops: list[str] = ["date", "time", "timetz"] _datetimelike_ops: list[str] = ( _field_ops + _object_ops + _bool_ops + _other_ops + ["unit"] ) _datetimelike_methods: list[str] = [ "to_period", "tz_localize", "tz_convert", "normalize", "strftime", "round", "floor", "ceil", "month_name", "day_name", "as_unit", ] # ndim is inherited from ExtensionArray, must exist to ensure # Timestamp.__richcmp__(DateTimeArray) operates pointwise # ensure that operations with numpy arrays defer to our implementation __array_priority__ = 1000 # ----------------------------------------------------------------- # Constructors _dtype: np.dtype[np.datetime64] | DatetimeTZDtype _freq: BaseOffset | None = None _default_dtype = DT64NS_DTYPE # used in TimeLikeOps.__init__ @classmethod def _from_scalars(cls, scalars, *, dtype: DtypeObj) -> Self: if lib.infer_dtype(scalars, skipna=True) not in ["datetime", "datetime64"]: # TODO: require any NAs be valid-for-DTA # TODO: if dtype is passed, check for tzawareness compat? raise ValueError return cls._from_sequence(scalars, dtype=dtype) @classmethod def _validate_dtype(cls, values, dtype): # used in TimeLikeOps.__init__ dtype = _validate_dt64_dtype(dtype) _validate_dt64_dtype(values.dtype) if isinstance(dtype, np.dtype): if values.dtype != dtype: raise ValueError("Values resolution does not match dtype.") else: vunit = np.datetime_data(values.dtype)[0] if vunit != dtype.unit: raise ValueError("Values resolution does not match dtype.") return dtype # error: Signature of "_simple_new" incompatible with supertype "NDArrayBacked" @classmethod def _simple_new( # type: ignore[override] cls, values: npt.NDArray[np.datetime64], freq: BaseOffset | None = None, dtype: np.dtype[np.datetime64] | DatetimeTZDtype = DT64NS_DTYPE, ) -> Self: assert isinstance(values, np.ndarray) assert dtype.kind == "M" if isinstance(dtype, np.dtype): assert dtype == values.dtype assert not is_unitless(dtype) else: # DatetimeTZDtype. If we have e.g. DatetimeTZDtype[us, UTC], # then values.dtype should be M8[us]. assert dtype._creso == get_unit_from_dtype(values.dtype) result = super()._simple_new(values, dtype) result._freq = freq return result @classmethod def _from_sequence(cls, scalars, *, dtype=None, copy: bool = False): return cls._from_sequence_not_strict(scalars, dtype=dtype, copy=copy) @classmethod def _from_sequence_not_strict( cls, data, *, dtype=None, copy: bool = False, tz=lib.no_default, freq: str | BaseOffset | lib.NoDefault | None = lib.no_default, dayfirst: bool = False, yearfirst: bool = False, ambiguous: TimeAmbiguous = "raise", ) -> Self: """ A non-strict version of _from_sequence, called from DatetimeIndex.__new__. """ # if the user either explicitly passes tz=None or a tz-naive dtype, we # disallows inferring a tz. explicit_tz_none = tz is None if tz is lib.no_default: tz = None else: tz = timezones.maybe_get_tz(tz) dtype = _validate_dt64_dtype(dtype) # if dtype has an embedded tz, capture it tz = _validate_tz_from_dtype(dtype, tz, explicit_tz_none) unit = None if dtype is not None: unit = dtl.dtype_to_unit(dtype) data, copy = dtl.ensure_arraylike_for_datetimelike( data, copy, cls_name="DatetimeArray" ) inferred_freq = None if isinstance(data, DatetimeArray): inferred_freq = data.freq subarr, tz = _sequence_to_dt64( data, copy=copy, tz=tz, dayfirst=dayfirst, yearfirst=yearfirst, ambiguous=ambiguous, out_unit=unit, ) # We have to call this again after possibly inferring a tz above _validate_tz_from_dtype(dtype, tz, explicit_tz_none) if tz is not None and explicit_tz_none: raise ValueError( "Passed data is timezone-aware, incompatible with 'tz=None'. " "Use obj.tz_localize(None) instead." ) data_unit = np.datetime_data(subarr.dtype)[0] data_dtype = tz_to_dtype(tz, data_unit) result = cls._simple_new(subarr, freq=inferred_freq, dtype=data_dtype) if unit is not None and unit != result.unit: # If unit was specified in user-passed dtype, cast to it here result = result.as_unit(unit) validate_kwds = {"ambiguous": ambiguous} result._maybe_pin_freq(freq, validate_kwds) return result @classmethod def _generate_range( cls, start, end, periods: int | None, freq, tz=None, normalize: bool = False, ambiguous: TimeAmbiguous = "raise", nonexistent: TimeNonexistent = "raise", inclusive: IntervalClosedType = "both", *, unit: str | None = None, ) -> Self: periods = dtl.validate_periods(periods) if freq is None and any(x is None for x in [periods, start, end]): raise ValueError("Must provide freq argument if no data is supplied") if com.count_not_none(start, end, periods, freq) != 3: raise ValueError( "Of the four parameters: start, end, periods, " "and freq, exactly three must be specified" ) freq = to_offset(freq) if start is not None: start = Timestamp(start) if end is not None: end = Timestamp(end) if start is NaT or end is NaT: raise ValueError("Neither `start` nor `end` can be NaT") if unit is not None: if unit not in ["s", "ms", "us", "ns"]: raise ValueError("'unit' must be one of 's', 'ms', 'us', 'ns'") else: unit = "ns" if start is not None: start = start.as_unit(unit, round_ok=False) if end is not None: end = end.as_unit(unit, round_ok=False) left_inclusive, right_inclusive = validate_inclusive(inclusive) start, end = _maybe_normalize_endpoints(start, end, normalize) tz = _infer_tz_from_endpoints(start, end, tz) if tz is not None: # Localize the start and end arguments start = _maybe_localize_point(start, freq, tz, ambiguous, nonexistent) end = _maybe_localize_point(end, freq, tz, ambiguous, nonexistent) if freq is not None: # We break Day arithmetic (fixed 24 hour) here and opt for # Day to mean calendar day (23/24/25 hour). Therefore, strip # tz info from start and day to avoid DST arithmetic if isinstance(freq, Day): if start is not None: start = start.tz_localize(None) if end is not None: end = end.tz_localize(None) if isinstance(freq, Tick): i8values = generate_regular_range(start, end, periods, freq, unit=unit) else: xdr = _generate_range( start=start, end=end, periods=periods, offset=freq, unit=unit ) i8values = np.array([x._value for x in xdr], dtype=np.int64) endpoint_tz = start.tz if start is not None else end.tz if tz is not None and endpoint_tz is None: if not timezones.is_utc(tz): # short-circuit tz_localize_to_utc which would make # an unnecessary copy with UTC but be a no-op. creso = abbrev_to_npy_unit(unit) i8values = tzconversion.tz_localize_to_utc( i8values, tz, ambiguous=ambiguous, nonexistent=nonexistent, creso=creso, ) # i8values is localized datetime64 array -> have to convert # start/end as well to compare if start is not None: start = start.tz_localize(tz, ambiguous, nonexistent) if end is not None: end = end.tz_localize(tz, ambiguous, nonexistent) else: # Create a linearly spaced date_range in local time # Nanosecond-granularity timestamps aren't always correctly # representable with doubles, so we limit the range that we # pass to np.linspace as much as possible periods = cast(int, periods) i8values = ( np.linspace(0, end._value - start._value, periods, dtype="int64") + start._value ) if i8values.dtype != "i8": # 2022-01-09 I (brock) am not sure if it is possible for this # to overflow and cast to e.g. f8, but if it does we need to cast i8values = i8values.astype("i8") if start == end: if not left_inclusive and not right_inclusive: i8values = i8values[1:-1] else: start_i8 = Timestamp(start)._value end_i8 = Timestamp(end)._value if not left_inclusive or not right_inclusive: if not left_inclusive and len(i8values) and i8values[0] == start_i8: i8values = i8values[1:] if not right_inclusive and len(i8values) and i8values[-1] == end_i8: i8values = i8values[:-1] dt64_values = i8values.view(f"datetime64[{unit}]") dtype = tz_to_dtype(tz, unit=unit) return cls._simple_new(dt64_values, freq=freq, dtype=dtype) # ----------------------------------------------------------------- # DatetimeLike Interface def _unbox_scalar(self, value) -> np.datetime64: if not isinstance(value, self._scalar_type) and value is not NaT: raise ValueError("'value' should be a Timestamp.") self._check_compatible_with(value) if value is NaT: return np.datetime64(value._value, self.unit) else: return value.as_unit(self.unit).asm8 def _scalar_from_string(self, value) -> Timestamp | NaTType: return Timestamp(value, tz=self.tz) def _check_compatible_with(self, other) -> None: if other is NaT: return self._assert_tzawareness_compat(other) # ----------------------------------------------------------------- # Descriptive Properties def _box_func(self, x: np.datetime64) -> Timestamp | NaTType: # GH#42228 value = x.view("i8") ts = Timestamp._from_value_and_reso(value, reso=self._creso, tz=self.tz) return ts @property # error: Return type "Union[dtype, DatetimeTZDtype]" of "dtype" # incompatible with return type "ExtensionDtype" in supertype # "ExtensionArray" def dtype(self) -> np.dtype[np.datetime64] | DatetimeTZDtype: # type: ignore[override] """ The dtype for the DatetimeArray. .. warning:: A future version of pandas will change dtype to never be a ``numpy.dtype``. Instead, :attr:`DatetimeArray.dtype` will always be an instance of an ``ExtensionDtype`` subclass. Returns ------- numpy.dtype or DatetimeTZDtype If the values are tz-naive, then ``np.dtype('datetime64[ns]')`` is returned. If the values are tz-aware, then the ``DatetimeTZDtype`` is returned. """ return self._dtype @property def tz(self) -> tzinfo | None: """ Return the timezone. Returns ------- datetime.tzinfo, pytz.tzinfo.BaseTZInfo, dateutil.tz.tz.tzfile, or None Returns None when the array is tz-naive. Examples -------- For Series: >>> s = pd.Series(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"]) >>> s = pd.to_datetime(s) >>> s 0 2020-01-01 10:00:00+00:00 1 2020-02-01 11:00:00+00:00 dtype: datetime64[ns, UTC] >>> s.dt.tz datetime.timezone.utc For DatetimeIndex: >>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00", ... "2/1/2020 11:00:00+00:00"]) >>> idx.tz datetime.timezone.utc """ # GH 18595 return getattr(self.dtype, "tz", None) @tz.setter def tz(self, value): # GH 3746: Prevent localizing or converting the index by setting tz raise AttributeError( "Cannot directly set timezone. Use tz_localize() " "or tz_convert() as appropriate" ) @property def tzinfo(self) -> tzinfo | None: """ Alias for tz attribute """ return self.tz @property # NB: override with cache_readonly in immutable subclasses def is_normalized(self) -> bool: """ Returns True if all of the dates are at midnight ("no time") """ return is_date_array_normalized(self.asi8, self.tz, reso=self._creso) @property # NB: override with cache_readonly in immutable subclasses def _resolution_obj(self) -> Resolution: return get_resolution(self.asi8, self.tz, reso=self._creso) # ---------------------------------------------------------------- # Array-Like / EA-Interface Methods def __array__(self, dtype=None, copy=None) -> np.ndarray: if dtype is None and self.tz: # The default for tz-aware is object, to preserve tz info dtype = object return super().__array__(dtype=dtype, copy=copy) def __iter__(self) -> Iterator: """ Return an iterator over the boxed values Yields ------ tstamp : Timestamp """ if self.ndim > 1: for i in range(len(self)): yield self[i] else: # convert in chunks of 10k for efficiency data = self.asi8 length = len(self) chunksize = _ITER_CHUNKSIZE chunks = (length // chunksize) + 1 for i in range(chunks): start_i = i * chunksize end_i = min((i + 1) * chunksize, length) converted = ints_to_pydatetime( data[start_i:end_i], tz=self.tz, box="timestamp", reso=self._creso, ) yield from converted def astype(self, dtype, copy: bool = True): # We handle # --> datetime # --> period # DatetimeLikeArrayMixin Super handles the rest. dtype = pandas_dtype(dtype) if dtype == self.dtype: if copy: return self.copy() return self elif isinstance(dtype, ExtensionDtype): if not isinstance(dtype, DatetimeTZDtype): # e.g. Sparse[datetime64[ns]] return super().astype(dtype, copy=copy) elif self.tz is None: # pre-2.0 this did self.tz_localize(dtype.tz), which did not match # the Series behavior which did # values.tz_localize("UTC").tz_convert(dtype.tz) raise TypeError( "Cannot use .astype to convert from timezone-naive dtype to " "timezone-aware dtype. Use obj.tz_localize instead or " "series.dt.tz_localize instead" ) else: # tzaware unit conversion e.g. datetime64[s, UTC] np_dtype = np.dtype(dtype.str) res_values = astype_overflowsafe(self._ndarray, np_dtype, copy=copy) return type(self)._simple_new(res_values, dtype=dtype, freq=self.freq) elif ( self.tz is None and lib.is_np_dtype(dtype, "M") and not is_unitless(dtype) and is_supported_dtype(dtype) ): # unit conversion e.g. datetime64[s] res_values = astype_overflowsafe(self._ndarray, dtype, copy=True) return type(self)._simple_new(res_values, dtype=res_values.dtype) # TODO: preserve freq? elif self.tz is not None and lib.is_np_dtype(dtype, "M"): # pre-2.0 behavior for DTA/DTI was # values.tz_convert("UTC").tz_localize(None), which did not match # the Series behavior raise TypeError( "Cannot use .astype to convert from timezone-aware dtype to " "timezone-naive dtype. Use obj.tz_localize(None) or " "obj.tz_convert('UTC').tz_localize(None) instead." ) elif ( self.tz is None and lib.is_np_dtype(dtype, "M") and dtype != self.dtype and is_unitless(dtype) ): raise TypeError( "Casting to unit-less dtype 'datetime64' is not supported. " "Pass e.g. 'datetime64[ns]' instead." ) elif isinstance(dtype, PeriodDtype): return self.to_period(freq=dtype.freq) return dtl.DatetimeLikeArrayMixin.astype(self, dtype, copy) # ----------------------------------------------------------------- # Rendering Methods def _format_native_types( self, *, na_rep: str | float = "NaT", date_format=None, **kwargs ) -> npt.NDArray[np.object_]: if date_format is None and self._is_dates_only: # Only dates and no timezone: provide a default format date_format = "%Y-%m-%d" return tslib.format_array_from_datetime( self.asi8, tz=self.tz, format=date_format, na_rep=na_rep, reso=self._creso ) # ----------------------------------------------------------------- # Comparison Methods def _has_same_tz(self, other) -> bool: # vzone shouldn't be None if value is non-datetime like if isinstance(other, np.datetime64): # convert to Timestamp as np.datetime64 doesn't have tz attr other = Timestamp(other) if not hasattr(other, "tzinfo"): return False other_tz = other.tzinfo return timezones.tz_compare(self.tzinfo, other_tz) def _assert_tzawareness_compat(self, other) -> None: # adapted from _Timestamp._assert_tzawareness_compat other_tz = getattr(other, "tzinfo", None) other_dtype = getattr(other, "dtype", None) if isinstance(other_dtype, DatetimeTZDtype): # Get tzinfo from Series dtype other_tz = other.dtype.tz if other is NaT: # pd.NaT quacks both aware and naive pass elif self.tz is None: if other_tz is not None: raise TypeError( "Cannot compare tz-naive and tz-aware datetime-like objects." ) elif other_tz is None: raise TypeError( "Cannot compare tz-naive and tz-aware datetime-like objects" ) # ----------------------------------------------------------------- # Arithmetic Methods def _add_offset(self, offset: BaseOffset) -> Self: assert not isinstance(offset, Tick) if self.tz is not None: values = self.tz_localize(None) else: values = self try: res_values = offset._apply_array(values._ndarray) if res_values.dtype.kind == "i": # error: Argument 1 to "view" of "ndarray" has incompatible type # "dtype[datetime64] | DatetimeTZDtype"; expected # "dtype[Any] | type[Any] | _SupportsDType[dtype[Any]]" res_values = res_values.view(values.dtype) # type: ignore[arg-type] except NotImplementedError: warnings.warn( "Non-vectorized DateOffset being applied to Series or DatetimeIndex.", PerformanceWarning, stacklevel=find_stack_level(), ) res_values = self.astype("O") + offset # TODO(GH#55564): as_unit will be unnecessary result = type(self)._from_sequence(res_values).as_unit(self.unit) if not len(self): # GH#30336 _from_sequence won't be able to infer self.tz return result.tz_localize(self.tz) else: result = type(self)._simple_new(res_values, dtype=res_values.dtype) if offset.normalize: result = result.normalize() result._freq = None if self.tz is not None: result = result.tz_localize(self.tz) return result # ----------------------------------------------------------------- # Timezone Conversion and Localization Methods def _local_timestamps(self) -> npt.NDArray[np.int64]: """ Convert to an i8 (unix-like nanosecond timestamp) representation while keeping the local timezone and not using UTC. This is used to calculate time-of-day information as if the timestamps were timezone-naive. """ if self.tz is None or timezones.is_utc(self.tz): # Avoid the copy that would be made in tzconversion return self.asi8 return tz_convert_from_utc(self.asi8, self.tz, reso=self._creso) def tz_convert(self, tz) -> Self: """ Convert tz-aware Datetime Array/Index from one time zone to another. Parameters ---------- tz : str, pytz.timezone, dateutil.tz.tzfile, datetime.tzinfo or None Time zone for time. Corresponding timestamps would be converted to this time zone of the Datetime Array/Index. A `tz` of None will convert to UTC and remove the timezone information. Returns ------- Array or Index Raises ------ TypeError If Datetime Array/Index is tz-naive. See Also -------- DatetimeIndex.tz : A timezone that has a variable offset from UTC. DatetimeIndex.tz_localize : Localize tz-naive DatetimeIndex to a given time zone, or remove timezone from a tz-aware DatetimeIndex. Examples -------- With the `tz` parameter, we can change the DatetimeIndex to other time zones: >>> dti = pd.date_range(start='2014-08-01 09:00', ... freq='h', periods=3, tz='Europe/Berlin') >>> dti DatetimeIndex(['2014-08-01 09:00:00+02:00', '2014-08-01 10:00:00+02:00', '2014-08-01 11:00:00+02:00'], dtype='datetime64[ns, Europe/Berlin]', freq='h') >>> dti.tz_convert('US/Central') DatetimeIndex(['2014-08-01 02:00:00-05:00', '2014-08-01 03:00:00-05:00', '2014-08-01 04:00:00-05:00'], dtype='datetime64[ns, US/Central]', freq='h') With the ``tz=None``, we can remove the timezone (after converting to UTC if necessary): >>> dti = pd.date_range(start='2014-08-01 09:00', freq='h', ... periods=3, tz='Europe/Berlin') >>> dti DatetimeIndex(['2014-08-01 09:00:00+02:00', '2014-08-01 10:00:00+02:00', '2014-08-01 11:00:00+02:00'], dtype='datetime64[ns, Europe/Berlin]', freq='h') >>> dti.tz_convert(None) DatetimeIndex(['2014-08-01 07:00:00', '2014-08-01 08:00:00', '2014-08-01 09:00:00'], dtype='datetime64[ns]', freq='h') """ tz = timezones.maybe_get_tz(tz) if self.tz is None: # tz naive, use tz_localize raise TypeError( "Cannot convert tz-naive timestamps, use tz_localize to localize" ) # No conversion since timestamps are all UTC to begin with dtype = tz_to_dtype(tz, unit=self.unit) return self._simple_new(self._ndarray, dtype=dtype, freq=self.freq) @dtl.ravel_compat def tz_localize( self, tz, ambiguous: TimeAmbiguous = "raise", nonexistent: TimeNonexistent = "raise", ) -> Self: """ Localize tz-naive Datetime Array/Index to tz-aware Datetime Array/Index. This method takes a time zone (tz) naive Datetime Array/Index object and makes this time zone aware. It does not move the time to another time zone. This method can also be used to do the inverse -- to create a time zone unaware object from an aware object. To that end, pass `tz=None`. Parameters ---------- tz : str, pytz.timezone, dateutil.tz.tzfile, datetime.tzinfo or None Time zone to convert timestamps to. Passing ``None`` will remove the time zone information preserving local time. ambiguous : 'infer', 'NaT', bool array, default 'raise' When clocks moved backward due to DST, ambiguous times may arise. For example in Central European Time (UTC+01), when going from 03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at 00:30:00 UTC and at 01:30:00 UTC. In such a situation, the `ambiguous` parameter dictates how ambiguous times should be handled. - 'infer' will attempt to infer fall dst-transition hours based on order - bool-ndarray where True signifies a DST time, False signifies 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 ------- Same type as self Array/Index converted to the specified time zone. Raises ------ TypeError If the Datetime Array/Index is tz-aware and tz is not None. See Also -------- DatetimeIndex.tz_convert : Convert tz-aware DatetimeIndex from one time zone to another. Examples -------- >>> tz_naive = pd.date_range('2018-03-01 09:00', periods=3) >>> tz_naive DatetimeIndex(['2018-03-01 09:00:00', '2018-03-02 09:00:00', '2018-03-03 09:00:00'], dtype='datetime64[ns]', freq='D') Localize DatetimeIndex in US/Eastern time zone: >>> tz_aware = tz_naive.tz_localize(tz='US/Eastern') >>> tz_aware DatetimeIndex(['2018-03-01 09:00:00-05:00', '2018-03-02 09:00:00-05:00', '2018-03-03 09:00:00-05:00'], dtype='datetime64[ns, US/Eastern]', freq=None) With the ``tz=None``, we can remove the time zone information while keeping the local time (not converted to UTC): >>> tz_aware.tz_localize(None) DatetimeIndex(['2018-03-01 09:00:00', '2018-03-02 09:00:00', '2018-03-03 09:00:00'], dtype='datetime64[ns]', freq=None) Be careful with DST changes. When there is sequential data, pandas can infer the DST time: >>> s = pd.to_datetime(pd.Series(['2018-10-28 01:30:00', ... '2018-10-28 02:00:00', ... '2018-10-28 02:30:00', ... '2018-10-28 02:00:00', ... '2018-10-28 02:30:00', ... '2018-10-28 03:00:00', ... '2018-10-28 03:30:00'])) >>> s.dt.tz_localize('CET', ambiguous='infer') 0 2018-10-28 01:30:00+02:00 1 2018-10-28 02:00:00+02:00 2 2018-10-28 02:30:00+02:00 3 2018-10-28 02:00:00+01:00 4 2018-10-28 02:30:00+01:00 5 2018-10-28 03:00:00+01:00 6 2018-10-28 03:30:00+01:00 dtype: datetime64[ns, CET] In some cases, inferring the DST is impossible. In such cases, you can pass an ndarray to the ambiguous parameter to set the DST explicitly >>> s = pd.to_datetime(pd.Series(['2018-10-28 01:20:00', ... '2018-10-28 02:36:00', ... '2018-10-28 03:46:00'])) >>> s.dt.tz_localize('CET', ambiguous=np.array([True, True, False])) 0 2018-10-28 01:20:00+02:00 1 2018-10-28 02:36:00+02:00 2 2018-10-28 03:46:00+01:00 dtype: datetime64[ns, CET] If the DST transition causes nonexistent times, you can shift these dates forward or backwards with a timedelta object or `'shift_forward'` or `'shift_backwards'`. >>> s = pd.to_datetime(pd.Series(['2015-03-29 02:30:00', ... '2015-03-29 03:30:00'])) >>> s.dt.tz_localize('Europe/Warsaw', nonexistent='shift_forward') 0 2015-03-29 03:00:00+02:00 1 2015-03-29 03:30:00+02:00 dtype: datetime64[ns, Europe/Warsaw] >>> s.dt.tz_localize('Europe/Warsaw', nonexistent='shift_backward') 0 2015-03-29 01:59:59.999999999+01:00 1 2015-03-29 03:30:00+02:00 dtype: datetime64[ns, Europe/Warsaw] >>> s.dt.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta('1h')) 0 2015-03-29 03:30:00+02:00 1 2015-03-29 03:30:00+02:00 dtype: datetime64[ns, Europe/Warsaw] """ nonexistent_options = ("raise", "NaT", "shift_forward", "shift_backward") if nonexistent not in nonexistent_options and not isinstance( nonexistent, timedelta ): raise ValueError( "The nonexistent argument must be one of 'raise', " "'NaT', 'shift_forward', 'shift_backward' or " "a timedelta object" ) if self.tz is not None: if tz is None: new_dates = tz_convert_from_utc(self.asi8, self.tz, reso=self._creso) else: raise TypeError("Already tz-aware, use tz_convert to convert.") else: tz = timezones.maybe_get_tz(tz) # Convert to UTC new_dates = tzconversion.tz_localize_to_utc( self.asi8, tz, ambiguous=ambiguous, nonexistent=nonexistent, creso=self._creso, ) new_dates_dt64 = new_dates.view(f"M8[{self.unit}]") dtype = tz_to_dtype(tz, unit=self.unit) freq = None if timezones.is_utc(tz) or (len(self) == 1 and not isna(new_dates_dt64[0])): # we can preserve freq # TODO: Also for fixed-offsets freq = self.freq elif tz is None and self.tz is None: # no-op freq = self.freq return self._simple_new(new_dates_dt64, dtype=dtype, freq=freq) # ---------------------------------------------------------------- # Conversion Methods - Vectorized analogues of Timestamp methods def to_pydatetime(self) -> npt.NDArray[np.object_]: """ Return an ndarray of ``datetime.datetime`` objects. Returns ------- numpy.ndarray Examples -------- >>> idx = pd.date_range('2018-02-27', periods=3) >>> idx.to_pydatetime() array([datetime.datetime(2018, 2, 27, 0, 0), datetime.datetime(2018, 2, 28, 0, 0), datetime.datetime(2018, 3, 1, 0, 0)], dtype=object) """ return ints_to_pydatetime(self.asi8, tz=self.tz, reso=self._creso) def normalize(self) -> Self: """ Convert times to midnight. The time component of the date-time is converted to midnight i.e. 00:00:00. This is useful in cases, when the time does not matter. Length is unaltered. The timezones are unaffected. This method is available on Series with datetime values under the ``.dt`` accessor, and directly on Datetime Array/Index. Returns ------- DatetimeArray, DatetimeIndex or Series The same type as the original data. Series will have the same name and index. DatetimeIndex will have the same name. See Also -------- floor : Floor the datetimes to the specified freq. ceil : Ceil the datetimes to the specified freq. round : Round the datetimes to the specified freq. Examples -------- >>> idx = pd.date_range(start='2014-08-01 10:00', freq='h', ... periods=3, tz='Asia/Calcutta') >>> idx DatetimeIndex(['2014-08-01 10:00:00+05:30', '2014-08-01 11:00:00+05:30', '2014-08-01 12:00:00+05:30'], dtype='datetime64[ns, Asia/Calcutta]', freq='h') >>> idx.normalize() DatetimeIndex(['2014-08-01 00:00:00+05:30', '2014-08-01 00:00:00+05:30', '2014-08-01 00:00:00+05:30'], dtype='datetime64[ns, Asia/Calcutta]', freq=None) """ new_values = normalize_i8_timestamps(self.asi8, self.tz, reso=self._creso) dt64_values = new_values.view(self._ndarray.dtype) dta = type(self)._simple_new(dt64_values, dtype=dt64_values.dtype) dta = dta._with_freq("infer") if self.tz is not None: dta = dta.tz_localize(self.tz) return dta def to_period(self, freq=None) -> PeriodArray: """ Cast to PeriodArray/PeriodIndex at a particular frequency. Converts DatetimeArray/Index to PeriodArray/PeriodIndex. Parameters ---------- freq : str or Period, optional One of pandas' :ref:`period aliases ` or an Period object. Will be inferred by default. Returns ------- PeriodArray/PeriodIndex Raises ------ ValueError When converting a DatetimeArray/Index with non-regular values, so that a frequency cannot be inferred. See Also -------- PeriodIndex: Immutable ndarray holding ordinal values. DatetimeIndex.to_pydatetime: Return DatetimeIndex as object. Examples -------- >>> df = pd.DataFrame({"y": [1, 2, 3]}, ... index=pd.to_datetime(["2000-03-31 00:00:00", ... "2000-05-31 00:00:00", ... "2000-08-31 00:00:00"])) >>> df.index.to_period("M") PeriodIndex(['2000-03', '2000-05', '2000-08'], dtype='period[M]') Infer the daily frequency >>> idx = pd.date_range("2017-01-01", periods=2) >>> idx.to_period() PeriodIndex(['2017-01-01', '2017-01-02'], dtype='period[D]') """ from pandas.core.arrays import PeriodArray if self.tz is not None: warnings.warn( "Converting to PeriodArray/Index representation " "will drop timezone information.", UserWarning, stacklevel=find_stack_level(), ) if freq is None: freq = self.freqstr or self.inferred_freq if isinstance(self.freq, BaseOffset) and hasattr( self.freq, "_period_dtype_code" ): freq = PeriodDtype(self.freq)._freqstr if freq is None: raise ValueError( "You must pass a freq argument as current index has none." ) res = get_period_alias(freq) # https://github.com/pandas-dev/pandas/issues/33358 if res is None: res = freq freq = res return PeriodArray._from_datetime64(self._ndarray, freq, tz=self.tz) # ----------------------------------------------------------------- # Properties - Vectorized Timestamp Properties/Methods def month_name(self, locale=None) -> npt.NDArray[np.object_]: """ Return the month names with specified locale. Parameters ---------- locale : str, optional Locale determining the language in which to return the month name. Default is English locale (``'en_US.utf8'``). Use the command ``locale -a`` on your terminal on Unix systems to find your locale language code. Returns ------- Series or Index Series or Index of month names. Examples -------- >>> s = pd.Series(pd.date_range(start='2018-01', freq='ME', periods=3)) >>> s 0 2018-01-31 1 2018-02-28 2 2018-03-31 dtype: datetime64[ns] >>> s.dt.month_name() 0 January 1 February 2 March dtype: object >>> idx = pd.date_range(start='2018-01', freq='ME', periods=3) >>> idx DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31'], dtype='datetime64[ns]', freq='ME') >>> idx.month_name() Index(['January', 'February', 'March'], dtype='object') Using the ``locale`` parameter you can set a different locale language, for example: ``idx.month_name(locale='pt_BR.utf8')`` will return month names in Brazilian Portuguese language. >>> idx = pd.date_range(start='2018-01', freq='ME', periods=3) >>> idx DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31'], dtype='datetime64[ns]', freq='ME') >>> idx.month_name(locale='pt_BR.utf8') # doctest: +SKIP Index(['Janeiro', 'Fevereiro', 'Março'], dtype='object') """ values = self._local_timestamps() result = fields.get_date_name_field( values, "month_name", locale=locale, reso=self._creso ) result = self._maybe_mask_results(result, fill_value=None) return result def day_name(self, locale=None) -> npt.NDArray[np.object_]: """ Return the day names with specified locale. Parameters ---------- locale : str, optional Locale determining the language in which to return the day name. Default is English locale (``'en_US.utf8'``). Use the command ``locale -a`` on your terminal on Unix systems to find your locale language code. Returns ------- Series or Index Series or Index of day names. Examples -------- >>> s = pd.Series(pd.date_range(start='2018-01-01', freq='D', periods=3)) >>> s 0 2018-01-01 1 2018-01-02 2 2018-01-03 dtype: datetime64[ns] >>> s.dt.day_name() 0 Monday 1 Tuesday 2 Wednesday dtype: object >>> idx = pd.date_range(start='2018-01-01', freq='D', periods=3) >>> idx DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'], dtype='datetime64[ns]', freq='D') >>> idx.day_name() Index(['Monday', 'Tuesday', 'Wednesday'], dtype='object') Using the ``locale`` parameter you can set a different locale language, for example: ``idx.day_name(locale='pt_BR.utf8')`` will return day names in Brazilian Portuguese language. >>> idx = pd.date_range(start='2018-01-01', freq='D', periods=3) >>> idx DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'], dtype='datetime64[ns]', freq='D') >>> idx.day_name(locale='pt_BR.utf8') # doctest: +SKIP Index(['Segunda', 'Terça', 'Quarta'], dtype='object') """ values = self._local_timestamps() result = fields.get_date_name_field( values, "day_name", locale=locale, reso=self._creso ) result = self._maybe_mask_results(result, fill_value=None) return result @property def time(self) -> npt.NDArray[np.object_]: """ Returns numpy array of :class:`datetime.time` objects. The time part of the Timestamps. Examples -------- For Series: >>> s = pd.Series(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"]) >>> s = pd.to_datetime(s) >>> s 0 2020-01-01 10:00:00+00:00 1 2020-02-01 11:00:00+00:00 dtype: datetime64[ns, UTC] >>> s.dt.time 0 10:00:00 1 11:00:00 dtype: object For DatetimeIndex: >>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00", ... "2/1/2020 11:00:00+00:00"]) >>> idx.time array([datetime.time(10, 0), datetime.time(11, 0)], dtype=object) """ # If the Timestamps have a timezone that is not UTC, # convert them into their i8 representation while # keeping their timezone and not using UTC timestamps = self._local_timestamps() return ints_to_pydatetime(timestamps, box="time", reso=self._creso) @property def timetz(self) -> npt.NDArray[np.object_]: """ Returns numpy array of :class:`datetime.time` objects with timezones. The time part of the Timestamps. Examples -------- For Series: >>> s = pd.Series(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"]) >>> s = pd.to_datetime(s) >>> s 0 2020-01-01 10:00:00+00:00 1 2020-02-01 11:00:00+00:00 dtype: datetime64[ns, UTC] >>> s.dt.timetz 0 10:00:00+00:00 1 11:00:00+00:00 dtype: object For DatetimeIndex: >>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00", ... "2/1/2020 11:00:00+00:00"]) >>> idx.timetz array([datetime.time(10, 0, tzinfo=datetime.timezone.utc), datetime.time(11, 0, tzinfo=datetime.timezone.utc)], dtype=object) """ return ints_to_pydatetime(self.asi8, self.tz, box="time", reso=self._creso) @property def date(self) -> npt.NDArray[np.object_]: """ Returns numpy array of python :class:`datetime.date` objects. Namely, the date part of Timestamps without time and timezone information. Examples -------- For Series: >>> s = pd.Series(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"]) >>> s = pd.to_datetime(s) >>> s 0 2020-01-01 10:00:00+00:00 1 2020-02-01 11:00:00+00:00 dtype: datetime64[ns, UTC] >>> s.dt.date 0 2020-01-01 1 2020-02-01 dtype: object For DatetimeIndex: >>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00", ... "2/1/2020 11:00:00+00:00"]) >>> idx.date array([datetime.date(2020, 1, 1), datetime.date(2020, 2, 1)], dtype=object) """ # If the Timestamps have a timezone that is not UTC, # convert them into their i8 representation while # keeping their timezone and not using UTC timestamps = self._local_timestamps() return ints_to_pydatetime(timestamps, box="date", reso=self._creso) def isocalendar(self) -> DataFrame: """ Calculate year, week, and day according to the ISO 8601 standard. Returns ------- DataFrame With columns year, week and day. See Also -------- Timestamp.isocalendar : Function return a 3-tuple containing ISO year, week number, and weekday for the given Timestamp object. datetime.date.isocalendar : Return a named tuple object with three components: year, week and weekday. Examples -------- >>> idx = pd.date_range(start='2019-12-29', freq='D', periods=4) >>> idx.isocalendar() year week day 2019-12-29 2019 52 7 2019-12-30 2020 1 1 2019-12-31 2020 1 2 2020-01-01 2020 1 3 >>> idx.isocalendar().week 2019-12-29 52 2019-12-30 1 2019-12-31 1 2020-01-01 1 Freq: D, Name: week, dtype: UInt32 """ from pandas import DataFrame values = self._local_timestamps() sarray = fields.build_isocalendar_sarray(values, reso=self._creso) iso_calendar_df = DataFrame( sarray, columns=["year", "week", "day"], dtype="UInt32" ) if self._hasna: iso_calendar_df.iloc[self._isnan] = None return iso_calendar_df year = _field_accessor( "year", "Y", """ The year of the datetime. Examples -------- >>> datetime_series = pd.Series( ... pd.date_range("2000-01-01", periods=3, freq="YE") ... ) >>> datetime_series 0 2000-12-31 1 2001-12-31 2 2002-12-31 dtype: datetime64[ns] >>> datetime_series.dt.year 0 2000 1 2001 2 2002 dtype: int32 """, ) month = _field_accessor( "month", "M", """ The month as January=1, December=12. Examples -------- >>> datetime_series = pd.Series( ... pd.date_range("2000-01-01", periods=3, freq="ME") ... ) >>> datetime_series 0 2000-01-31 1 2000-02-29 2 2000-03-31 dtype: datetime64[ns] >>> datetime_series.dt.month 0 1 1 2 2 3 dtype: int32 """, ) day = _field_accessor( "day", "D", """ The day of the datetime. Examples -------- >>> datetime_series = pd.Series( ... pd.date_range("2000-01-01", periods=3, freq="D") ... ) >>> datetime_series 0 2000-01-01 1 2000-01-02 2 2000-01-03 dtype: datetime64[ns] >>> datetime_series.dt.day 0 1 1 2 2 3 dtype: int32 """, ) hour = _field_accessor( "hour", "h", """ The hours of the datetime. Examples -------- >>> datetime_series = pd.Series( ... pd.date_range("2000-01-01", periods=3, freq="h") ... ) >>> datetime_series 0 2000-01-01 00:00:00 1 2000-01-01 01:00:00 2 2000-01-01 02:00:00 dtype: datetime64[ns] >>> datetime_series.dt.hour 0 0 1 1 2 2 dtype: int32 """, ) minute = _field_accessor( "minute", "m", """ The minutes of the datetime. Examples -------- >>> datetime_series = pd.Series( ... pd.date_range("2000-01-01", periods=3, freq="min") ... ) >>> datetime_series 0 2000-01-01 00:00:00 1 2000-01-01 00:01:00 2 2000-01-01 00:02:00 dtype: datetime64[ns] >>> datetime_series.dt.minute 0 0 1 1 2 2 dtype: int32 """, ) second = _field_accessor( "second", "s", """ The seconds of the datetime. Examples -------- >>> datetime_series = pd.Series( ... pd.date_range("2000-01-01", periods=3, freq="s") ... ) >>> datetime_series 0 2000-01-01 00:00:00 1 2000-01-01 00:00:01 2 2000-01-01 00:00:02 dtype: datetime64[ns] >>> datetime_series.dt.second 0 0 1 1 2 2 dtype: int32 """, ) microsecond = _field_accessor( "microsecond", "us", """ The microseconds of the datetime. Examples -------- >>> datetime_series = pd.Series( ... pd.date_range("2000-01-01", periods=3, freq="us") ... ) >>> datetime_series 0 2000-01-01 00:00:00.000000 1 2000-01-01 00:00:00.000001 2 2000-01-01 00:00:00.000002 dtype: datetime64[ns] >>> datetime_series.dt.microsecond 0 0 1 1 2 2 dtype: int32 """, ) nanosecond = _field_accessor( "nanosecond", "ns", """ The nanoseconds of the datetime. Examples -------- >>> datetime_series = pd.Series( ... pd.date_range("2000-01-01", periods=3, freq="ns") ... ) >>> datetime_series 0 2000-01-01 00:00:00.000000000 1 2000-01-01 00:00:00.000000001 2 2000-01-01 00:00:00.000000002 dtype: datetime64[ns] >>> datetime_series.dt.nanosecond 0 0 1 1 2 2 dtype: int32 """, ) _dayofweek_doc = """ The day of the week with Monday=0, Sunday=6. Return the day of the week. It is assumed the week starts on Monday, which is denoted by 0 and ends on Sunday which is denoted by 6. This method is available on both Series with datetime values (using the `dt` accessor) or DatetimeIndex. Returns ------- Series or Index Containing integers indicating the day number. See Also -------- Series.dt.dayofweek : Alias. Series.dt.weekday : Alias. Series.dt.day_name : Returns the name of the day of the week. Examples -------- >>> s = pd.date_range('2016-12-31', '2017-01-08', freq='D').to_series() >>> s.dt.dayofweek 2016-12-31 5 2017-01-01 6 2017-01-02 0 2017-01-03 1 2017-01-04 2 2017-01-05 3 2017-01-06 4 2017-01-07 5 2017-01-08 6 Freq: D, dtype: int32 """ day_of_week = _field_accessor("day_of_week", "dow", _dayofweek_doc) dayofweek = day_of_week weekday = day_of_week day_of_year = _field_accessor( "dayofyear", "doy", """ The ordinal day of the year. Examples -------- For Series: >>> s = pd.Series(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"]) >>> s = pd.to_datetime(s) >>> s 0 2020-01-01 10:00:00+00:00 1 2020-02-01 11:00:00+00:00 dtype: datetime64[ns, UTC] >>> s.dt.dayofyear 0 1 1 32 dtype: int32 For DatetimeIndex: >>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00", ... "2/1/2020 11:00:00+00:00"]) >>> idx.dayofyear Index([1, 32], dtype='int32') """, ) dayofyear = day_of_year quarter = _field_accessor( "quarter", "q", """ The quarter of the date. Examples -------- For Series: >>> s = pd.Series(["1/1/2020 10:00:00+00:00", "4/1/2020 11:00:00+00:00"]) >>> s = pd.to_datetime(s) >>> s 0 2020-01-01 10:00:00+00:00 1 2020-04-01 11:00:00+00:00 dtype: datetime64[ns, UTC] >>> s.dt.quarter 0 1 1 2 dtype: int32 For DatetimeIndex: >>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00", ... "2/1/2020 11:00:00+00:00"]) >>> idx.quarter Index([1, 1], dtype='int32') """, ) days_in_month = _field_accessor( "days_in_month", "dim", """ The number of days in the month. Examples -------- >>> s = pd.Series(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"]) >>> s = pd.to_datetime(s) >>> s 0 2020-01-01 10:00:00+00:00 1 2020-02-01 11:00:00+00:00 dtype: datetime64[ns, UTC] >>> s.dt.daysinmonth 0 31 1 29 dtype: int32 """, ) daysinmonth = days_in_month _is_month_doc = """ Indicates whether the date is the {first_or_last} day of the month. Returns ------- Series or array For Series, returns a Series with boolean values. For DatetimeIndex, returns a boolean array. See Also -------- is_month_start : Return a boolean indicating whether the date is the first day of the month. is_month_end : Return a boolean indicating whether the date is the last day of the month. Examples -------- This method is available on Series with datetime values under the ``.dt`` accessor, and directly on DatetimeIndex. >>> s = pd.Series(pd.date_range("2018-02-27", periods=3)) >>> s 0 2018-02-27 1 2018-02-28 2 2018-03-01 dtype: datetime64[ns] >>> s.dt.is_month_start 0 False 1 False 2 True dtype: bool >>> s.dt.is_month_end 0 False 1 True 2 False dtype: bool >>> idx = pd.date_range("2018-02-27", periods=3) >>> idx.is_month_start array([False, False, True]) >>> idx.is_month_end array([False, True, False]) """ is_month_start = _field_accessor( "is_month_start", "is_month_start", _is_month_doc.format(first_or_last="first") ) is_month_end = _field_accessor( "is_month_end", "is_month_end", _is_month_doc.format(first_or_last="last") ) is_quarter_start = _field_accessor( "is_quarter_start", "is_quarter_start", """ Indicator for whether the date is the first day of a quarter. Returns ------- is_quarter_start : Series or DatetimeIndex The same type as the original data with boolean values. Series will have the same name and index. DatetimeIndex will have the same name. See Also -------- quarter : Return the quarter of the date. is_quarter_end : Similar property for indicating the quarter end. Examples -------- This method is available on Series with datetime values under the ``.dt`` accessor, and directly on DatetimeIndex. >>> df = pd.DataFrame({'dates': pd.date_range("2017-03-30", ... periods=4)}) >>> df.assign(quarter=df.dates.dt.quarter, ... is_quarter_start=df.dates.dt.is_quarter_start) dates quarter is_quarter_start 0 2017-03-30 1 False 1 2017-03-31 1 False 2 2017-04-01 2 True 3 2017-04-02 2 False >>> idx = pd.date_range('2017-03-30', periods=4) >>> idx DatetimeIndex(['2017-03-30', '2017-03-31', '2017-04-01', '2017-04-02'], dtype='datetime64[ns]', freq='D') >>> idx.is_quarter_start array([False, False, True, False]) """, ) is_quarter_end = _field_accessor( "is_quarter_end", "is_quarter_end", """ Indicator for whether the date is the last day of a quarter. Returns ------- is_quarter_end : Series or DatetimeIndex The same type as the original data with boolean values. Series will have the same name and index. DatetimeIndex will have the same name. See Also -------- quarter : Return the quarter of the date. is_quarter_start : Similar property indicating the quarter start. Examples -------- This method is available on Series with datetime values under the ``.dt`` accessor, and directly on DatetimeIndex. >>> df = pd.DataFrame({'dates': pd.date_range("2017-03-30", ... periods=4)}) >>> df.assign(quarter=df.dates.dt.quarter, ... is_quarter_end=df.dates.dt.is_quarter_end) dates quarter is_quarter_end 0 2017-03-30 1 False 1 2017-03-31 1 True 2 2017-04-01 2 False 3 2017-04-02 2 False >>> idx = pd.date_range('2017-03-30', periods=4) >>> idx DatetimeIndex(['2017-03-30', '2017-03-31', '2017-04-01', '2017-04-02'], dtype='datetime64[ns]', freq='D') >>> idx.is_quarter_end array([False, True, False, False]) """, ) is_year_start = _field_accessor( "is_year_start", "is_year_start", """ Indicate whether the date is the first day of a year. Returns ------- Series or DatetimeIndex The same type as the original data with boolean values. Series will have the same name and index. DatetimeIndex will have the same name. See Also -------- is_year_end : Similar property indicating the last day of the year. Examples -------- This method is available on Series with datetime values under the ``.dt`` accessor, and directly on DatetimeIndex. >>> dates = pd.Series(pd.date_range("2017-12-30", periods=3)) >>> dates 0 2017-12-30 1 2017-12-31 2 2018-01-01 dtype: datetime64[ns] >>> dates.dt.is_year_start 0 False 1 False 2 True dtype: bool >>> idx = pd.date_range("2017-12-30", periods=3) >>> idx DatetimeIndex(['2017-12-30', '2017-12-31', '2018-01-01'], dtype='datetime64[ns]', freq='D') >>> idx.is_year_start array([False, False, True]) """, ) is_year_end = _field_accessor( "is_year_end", "is_year_end", """ Indicate whether the date is the last day of the year. Returns ------- Series or DatetimeIndex The same type as the original data with boolean values. Series will have the same name and index. DatetimeIndex will have the same name. See Also -------- is_year_start : Similar property indicating the start of the year. Examples -------- This method is available on Series with datetime values under the ``.dt`` accessor, and directly on DatetimeIndex. >>> dates = pd.Series(pd.date_range("2017-12-30", periods=3)) >>> dates 0 2017-12-30 1 2017-12-31 2 2018-01-01 dtype: datetime64[ns] >>> dates.dt.is_year_end 0 False 1 True 2 False dtype: bool >>> idx = pd.date_range("2017-12-30", periods=3) >>> idx DatetimeIndex(['2017-12-30', '2017-12-31', '2018-01-01'], dtype='datetime64[ns]', freq='D') >>> idx.is_year_end array([False, True, False]) """, ) is_leap_year = _field_accessor( "is_leap_year", "is_leap_year", """ Boolean indicator if the date belongs to a leap year. A leap year is a year, which has 366 days (instead of 365) including 29th of February as an intercalary day. Leap years are years which are multiples of four with the exception of years divisible by 100 but not by 400. Returns ------- Series or ndarray Booleans indicating if dates belong to a leap year. Examples -------- This method is available on Series with datetime values under the ``.dt`` accessor, and directly on DatetimeIndex. >>> idx = pd.date_range("2012-01-01", "2015-01-01", freq="YE") >>> idx DatetimeIndex(['2012-12-31', '2013-12-31', '2014-12-31'], dtype='datetime64[ns]', freq='YE-DEC') >>> idx.is_leap_year array([ True, False, False]) >>> dates_series = pd.Series(idx) >>> dates_series 0 2012-12-31 1 2013-12-31 2 2014-12-31 dtype: datetime64[ns] >>> dates_series.dt.is_leap_year 0 True 1 False 2 False dtype: bool """, ) def to_julian_date(self) -> npt.NDArray[np.float64]: """ Convert Datetime Array to float64 ndarray of Julian Dates. 0 Julian date is noon January 1, 4713 BC. https://en.wikipedia.org/wiki/Julian_day """ # http://mysite.verizon.net/aesir_research/date/jdalg2.htm year = np.asarray(self.year) month = np.asarray(self.month) day = np.asarray(self.day) testarr = month < 3 year[testarr] -= 1 month[testarr] += 12 return ( day + np.fix((153 * month - 457) / 5) + 365 * year + np.floor(year / 4) - np.floor(year / 100) + np.floor(year / 400) + 1_721_118.5 + ( self.hour + self.minute / 60 + self.second / 3600 + self.microsecond / 3600 / 10**6 + self.nanosecond / 3600 / 10**9 ) / 24 ) # ----------------------------------------------------------------- # Reductions def std( self, axis=None, dtype=None, out=None, ddof: int = 1, keepdims: bool = False, skipna: bool = True, ): """ Return sample standard deviation over requested axis. Normalized by `N-1` by default. This can be changed using ``ddof``. Parameters ---------- axis : int, optional Axis for the function to be applied on. For :class:`pandas.Series` this parameter is unused and defaults to ``None``. ddof : int, default 1 Degrees of Freedom. The divisor used in calculations is `N - ddof`, where `N` represents the number of elements. skipna : bool, default True Exclude NA/null values. If an entire row/column is ``NA``, the result will be ``NA``. Returns ------- Timedelta See Also -------- numpy.ndarray.std : Returns the standard deviation of the array elements along given axis. Series.std : Return sample standard deviation over requested axis. Examples -------- For :class:`pandas.DatetimeIndex`: >>> idx = pd.date_range('2001-01-01 00:00', periods=3) >>> idx DatetimeIndex(['2001-01-01', '2001-01-02', '2001-01-03'], dtype='datetime64[ns]', freq='D') >>> idx.std() Timedelta('1 days 00:00:00') """ # Because std is translation-invariant, we can get self.std # by calculating (self - Timestamp(0)).std, and we can do it # without creating a copy by using a view on self._ndarray from pandas.core.arrays import TimedeltaArray # Find the td64 dtype with the same resolution as our dt64 dtype dtype_str = self._ndarray.dtype.name.replace("datetime64", "timedelta64") dtype = np.dtype(dtype_str) tda = TimedeltaArray._simple_new(self._ndarray.view(dtype), dtype=dtype) return tda.std(axis=axis, out=out, ddof=ddof, keepdims=keepdims, skipna=skipna) # ------------------------------------------------------------------- # Constructor Helpers def _sequence_to_dt64( data: ArrayLike, *, copy: bool = False, tz: tzinfo | None = None, dayfirst: bool = False, yearfirst: bool = False, ambiguous: TimeAmbiguous = "raise", out_unit: str | None = None, ): """ Parameters ---------- data : np.ndarray or ExtensionArray dtl.ensure_arraylike_for_datetimelike has already been called. copy : bool, default False tz : tzinfo or None, default None dayfirst : bool, default False yearfirst : bool, default False ambiguous : str, bool, or arraylike, default 'raise' See pandas._libs.tslibs.tzconversion.tz_localize_to_utc. out_unit : str or None, default None Desired output resolution. Returns ------- result : numpy.ndarray The sequence converted to a numpy array with dtype ``datetime64[unit]``. Where `unit` is "ns" unless specified otherwise by `out_unit`. tz : tzinfo or None Either the user-provided tzinfo or one inferred from the data. Raises ------ TypeError : PeriodDType data is passed """ # By this point we are assured to have either a numpy array or Index data, copy = maybe_convert_dtype(data, copy, tz=tz) data_dtype = getattr(data, "dtype", None) if out_unit is None: out_unit = "ns" out_dtype = np.dtype(f"M8[{out_unit}]") if data_dtype == object or is_string_dtype(data_dtype): # TODO: We do not have tests specific to string-dtypes, # also complex or categorical or other extension data = cast(np.ndarray, data) copy = False if lib.infer_dtype(data, skipna=False) == "integer": # Much more performant than going through array_to_datetime data = data.astype(np.int64) elif tz is not None and ambiguous == "raise": obj_data = np.asarray(data, dtype=object) result = tslib.array_to_datetime_with_tz( obj_data, tz=tz, dayfirst=dayfirst, yearfirst=yearfirst, creso=abbrev_to_npy_unit(out_unit), ) return result, tz else: converted, inferred_tz = objects_to_datetime64( data, dayfirst=dayfirst, yearfirst=yearfirst, allow_object=False, out_unit=out_unit or "ns", ) copy = False if tz and inferred_tz: # two timezones: convert to intended from base UTC repr # GH#42505 by convention, these are _already_ UTC result = converted elif inferred_tz: tz = inferred_tz result = converted else: result, _ = _construct_from_dt64_naive( converted, tz=tz, copy=copy, ambiguous=ambiguous ) return result, tz data_dtype = data.dtype # `data` may have originally been a Categorical[datetime64[ns, tz]], # so we need to handle these types. if isinstance(data_dtype, DatetimeTZDtype): # DatetimeArray -> ndarray data = cast(DatetimeArray, data) tz = _maybe_infer_tz(tz, data.tz) result = data._ndarray elif lib.is_np_dtype(data_dtype, "M"): # tz-naive DatetimeArray or ndarray[datetime64] if isinstance(data, DatetimeArray): data = data._ndarray data = cast(np.ndarray, data) result, copy = _construct_from_dt64_naive( data, tz=tz, copy=copy, ambiguous=ambiguous ) else: # must be integer dtype otherwise # assume this data are epoch timestamps if data.dtype != INT64_DTYPE: data = data.astype(np.int64, copy=False) copy = False data = cast(np.ndarray, data) result = data.view(out_dtype) if copy: result = result.copy() assert isinstance(result, np.ndarray), type(result) assert result.dtype.kind == "M" assert result.dtype != "M8" assert is_supported_dtype(result.dtype) return result, tz def _construct_from_dt64_naive( data: np.ndarray, *, tz: tzinfo | None, copy: bool, ambiguous: TimeAmbiguous ) -> tuple[np.ndarray, bool]: """ Convert datetime64 data to a supported dtype, localizing if necessary. """ # Caller is responsible for ensuring # lib.is_np_dtype(data.dtype) new_dtype = data.dtype if not is_supported_dtype(new_dtype): # Cast to the nearest supported unit, generally "s" new_dtype = get_supported_dtype(new_dtype) data = astype_overflowsafe(data, dtype=new_dtype, copy=False) copy = False if data.dtype.byteorder == ">": # TODO: better way to handle this? non-copying alternative? # without this, test_constructor_datetime64_bigendian fails data = data.astype(data.dtype.newbyteorder("<")) new_dtype = data.dtype copy = False if tz is not None: # Convert tz-naive to UTC # TODO: if tz is UTC, are there situations where we *don't* want a # copy? tz_localize_to_utc always makes one. shape = data.shape if data.ndim > 1: data = data.ravel() data_unit = get_unit_from_dtype(new_dtype) data = tzconversion.tz_localize_to_utc( data.view("i8"), tz, ambiguous=ambiguous, creso=data_unit ) data = data.view(new_dtype) data = data.reshape(shape) assert data.dtype == new_dtype, data.dtype result = data return result, copy def objects_to_datetime64( data: np.ndarray, dayfirst, yearfirst, utc: bool = False, errors: DateTimeErrorChoices = "raise", allow_object: bool = False, out_unit: str = "ns", ): """ Convert data to array of timestamps. Parameters ---------- data : np.ndarray[object] dayfirst : bool yearfirst : bool utc : bool, default False Whether to convert/localize timestamps to UTC. errors : {'raise', 'ignore', 'coerce'} allow_object : bool Whether to return an object-dtype ndarray instead of raising if the data contains more than one timezone. out_unit : str, default "ns" Returns ------- result : ndarray np.datetime64[out_unit] if returned values represent wall times or UTC timestamps. object if mixed timezones inferred_tz : tzinfo or None If not None, then the datetime64 values in `result` denote UTC timestamps. Raises ------ ValueError : if data cannot be converted to datetimes TypeError : When a type cannot be converted to datetime """ assert errors in ["raise", "ignore", "coerce"] # if str-dtype, convert data = np.asarray(data, dtype=np.object_) result, tz_parsed = tslib.array_to_datetime( data, errors=errors, utc=utc, dayfirst=dayfirst, yearfirst=yearfirst, creso=abbrev_to_npy_unit(out_unit), ) if tz_parsed is not None: # We can take a shortcut since the datetime64 numpy array # is in UTC return result, tz_parsed elif result.dtype.kind == "M": return result, tz_parsed elif result.dtype == object: # GH#23675 when called via `pd.to_datetime`, returning an object-dtype # array is allowed. When called via `pd.DatetimeIndex`, we can # only accept datetime64 dtype, so raise TypeError if object-dtype # is returned, as that indicates the values can be recognized as # datetimes but they have conflicting timezones/awareness if allow_object: return result, tz_parsed raise TypeError("DatetimeIndex has mixed timezones") else: # pragma: no cover # GH#23675 this TypeError should never be hit, whereas the TypeError # in the object-dtype branch above is reachable. raise TypeError(result) def maybe_convert_dtype(data, copy: bool, tz: tzinfo | None = None): """ Convert data based on dtype conventions, issuing errors where appropriate. Parameters ---------- data : np.ndarray or pd.Index copy : bool tz : tzinfo or None, default None Returns ------- data : np.ndarray or pd.Index copy : bool Raises ------ TypeError : PeriodDType data is passed """ 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: """ If a timezone is inferred from data, check that it is compatible with the user-provided timezone, if any. Parameters ---------- tz : tzinfo or None inferred_tz : tzinfo or None Returns ------- tz : tzinfo or None Raises ------ TypeError : if both timezones are present but do not match """ if tz is None: tz = inferred_tz elif inferred_tz is None: pass elif not timezones.tz_compare(tz, inferred_tz): raise TypeError( f"data is already tz-aware {inferred_tz}, unable to " f"set specified tz: {tz}" ) return tz def _validate_dt64_dtype(dtype): """ Check that a dtype, if passed, represents either a numpy datetime64[ns] dtype or a pandas DatetimeTZDtype. Parameters ---------- dtype : object Returns ------- dtype : None, numpy.dtype, or DatetimeTZDtype Raises ------ ValueError : invalid dtype Notes ----- Unlike _validate_tz_from_dtype, this does _not_ allow non-existent tz errors to go through """ if dtype is not None: dtype = pandas_dtype(dtype) if dtype == np.dtype("M8"): # no precision, disallowed GH#24806 msg = ( "Passing in 'datetime64' dtype with no precision is not allowed. " "Please pass in 'datetime64[ns]' instead." ) raise ValueError(msg) if ( isinstance(dtype, np.dtype) and (dtype.kind != "M" or not is_supported_dtype(dtype)) ) or not isinstance(dtype, (np.dtype, DatetimeTZDtype)): raise ValueError( f"Unexpected value for 'dtype': '{dtype}'. " "Must be 'datetime64[s]', 'datetime64[ms]', 'datetime64[us]', " "'datetime64[ns]' or DatetimeTZDtype'." ) if getattr(dtype, "tz", None): # https://github.com/pandas-dev/pandas/issues/18595 # Ensure that we have a standard timezone for pytz objects. # Without this, things like adding an array of timedeltas and # a tz-aware Timestamp (with a tz specific to its datetime) will # be incorrect(ish?) for the array as a whole dtype = cast(DatetimeTZDtype, dtype) dtype = DatetimeTZDtype( unit=dtype.unit, tz=timezones.tz_standardize(dtype.tz) ) return dtype def _validate_tz_from_dtype( dtype, tz: tzinfo | None, explicit_tz_none: bool = False ) -> tzinfo | None: """ If the given dtype is a DatetimeTZDtype, extract the implied tzinfo object from it and check that it does not conflict with the given tz. Parameters ---------- dtype : dtype, str tz : None, tzinfo explicit_tz_none : bool, default False Whether tz=None was passed explicitly, as opposed to lib.no_default. Returns ------- tz : consensus tzinfo Raises ------ ValueError : on tzinfo mismatch """ if dtype is not None: if isinstance(dtype, str): try: dtype = DatetimeTZDtype.construct_from_string(dtype) except TypeError: # Things like `datetime64[ns]`, which is OK for the # constructors, but also nonsense, which should be validated # but not by us. We *do* allow non-existent tz errors to # go through pass dtz = getattr(dtype, "tz", None) if dtz is not None: if tz is not None and not timezones.tz_compare(tz, dtz): raise ValueError("cannot supply both a tz and a dtype with a tz") if explicit_tz_none: raise ValueError("Cannot pass both a timezone-aware dtype and tz=None") tz = dtz if tz is not None and lib.is_np_dtype(dtype, "M"): # We also need to check for the case where the user passed a # tz-naive dtype (i.e. datetime64[ns]) if tz is not None and not timezones.tz_compare(tz, dtz): raise ValueError( "cannot supply both a tz and a " "timezone-naive dtype (i.e. datetime64[ns])" ) return tz def _infer_tz_from_endpoints( start: Timestamp, end: Timestamp, tz: tzinfo | None ) -> tzinfo | None: """ If a timezone is not explicitly given via `tz`, see if one can be inferred from the `start` and `end` endpoints. If more than one of these inputs provides a timezone, require that they all agree. Parameters ---------- start : Timestamp end : Timestamp tz : tzinfo or None Returns ------- tz : tzinfo or None Raises ------ TypeError : if start and end timezones do not agree """ try: inferred_tz = timezones.infer_tzinfo(start, end) except AssertionError as err: # infer_tzinfo raises AssertionError if passed mismatched timezones raise TypeError( "Start and end cannot both be tz-aware with different timezones" ) from err inferred_tz = timezones.maybe_get_tz(inferred_tz) tz = timezones.maybe_get_tz(tz) if tz is not None and inferred_tz is not None: if not timezones.tz_compare(inferred_tz, tz): raise AssertionError("Inferred time zone not equal to passed time zone") elif inferred_tz is not None: tz = inferred_tz return tz def _maybe_normalize_endpoints( start: Timestamp | None, end: Timestamp | None, normalize: bool ): if normalize: if start is not None: start = start.normalize() if end is not None: end = end.normalize() return start, end def _maybe_localize_point( ts: Timestamp | None, freq, tz, ambiguous, nonexistent ) -> Timestamp | None: """ Localize a start or end Timestamp to the timezone of the corresponding start or end Timestamp Parameters ---------- ts : start or end Timestamp to potentially localize freq : Tick, DateOffset, or None tz : str, timezone object or None ambiguous: str, localization behavior for ambiguous times nonexistent: str, localization behavior for nonexistent times Returns ------- ts : Timestamp """ # Make sure start and end are timezone localized if: # 1) freq = a Timedelta-like frequency (Tick) # 2) freq = None i.e. generating a linspaced range if ts is not None and ts.tzinfo is None: # Note: We can't ambiguous='infer' a singular ambiguous time; however, # we have historically defaulted ambiguous=False ambiguous = ambiguous if ambiguous != "infer" else False localize_args = {"ambiguous": ambiguous, "nonexistent": nonexistent, "tz": None} if isinstance(freq, Tick) or freq is None: localize_args["tz"] = tz ts = ts.tz_localize(**localize_args) return ts def _generate_range( start: Timestamp | None, end: Timestamp | None, periods: int | None, offset: BaseOffset, *, unit: str, ): """ Generates a sequence of dates corresponding to the specified time offset. Similar to dateutil.rrule except uses pandas DateOffset objects to represent time increments. Parameters ---------- start : Timestamp or None end : Timestamp or None periods : int or None offset : DateOffset unit : str Notes ----- * This method is faster for generating weekdays than dateutil.rrule * At least two of (start, end, periods) must be specified. * If both start and end are specified, the returned dates will satisfy start <= date <= end. Returns ------- dates : generator object """ offset = to_offset(offset) # Argument 1 to "Timestamp" has incompatible type "Optional[Timestamp]"; # expected "Union[integer[Any], float, str, date, datetime64]" start = Timestamp(start) # type: ignore[arg-type] if start is not NaT: start = start.as_unit(unit) else: start = None # Argument 1 to "Timestamp" has incompatible type "Optional[Timestamp]"; # expected "Union[integer[Any], float, str, date, datetime64]" end = Timestamp(end) # type: ignore[arg-type] if end is not NaT: end = end.as_unit(unit) else: end = None if start and not offset.is_on_offset(start): # Incompatible types in assignment (expression has type "datetime", # variable has type "Optional[Timestamp]") start = offset.rollforward(start) # type: ignore[assignment] elif end and not offset.is_on_offset(end): # Incompatible types in assignment (expression has type "datetime", # variable has type "Optional[Timestamp]") end = offset.rollback(end) # type: ignore[assignment] # Unsupported operand types for < ("Timestamp" and "None") if periods is None and end < start and offset.n >= 0: # type: ignore[operator] end = None periods = 0 if end is None: # error: No overload variant of "__radd__" of "BaseOffset" matches # argument type "None" end = start + (periods - 1) * offset # type: ignore[operator] if start is None: # error: No overload variant of "__radd__" of "BaseOffset" matches # argument type "None" start = end - (periods - 1) * offset # type: ignore[operator] start = cast(Timestamp, start) end = cast(Timestamp, end) cur = start if offset.n >= 0: while cur <= end: yield cur if cur == end: # GH#24252 avoid overflows by not performing the addition # in offset.apply unless we have to break # faster than cur + offset next_date = offset._apply(cur) next_date = next_date.as_unit(unit) if next_date <= cur: raise ValueError(f"Offset {offset} did not increment date") cur = next_date else: while cur >= end: yield cur if cur == end: # GH#24252 avoid overflows by not performing the addition # in offset.apply unless we have to break # faster than cur + offset next_date = offset._apply(cur) next_date = next_date.as_unit(unit) if next_date >= cur: raise ValueError(f"Offset {offset} did not decrement date") cur = next_date