from __future__ import annotations import datetime as dt import operator from typing import ( TYPE_CHECKING, Self, ) import warnings import numpy as np from pandas._libs import ( NaT, Period, Timestamp, index as libindex, lib, ) from pandas._libs.tslibs import ( Resolution, Tick, Timedelta, periods_per_day, timezones, to_offset, ) from pandas._libs.tslibs.dtypes import abbrev_to_npy_unit from pandas._libs.tslibs.offsets import ( DateOffset, prefix_mapping, ) from pandas.errors import Pandas4Warning from pandas.util._decorators import ( cache_readonly, set_module, ) from pandas.util._exceptions import find_stack_level from pandas.core.dtypes.common import is_scalar from pandas.core.dtypes.dtypes import ( ArrowDtype, DatetimeTZDtype, ) from pandas.core.dtypes.generic import ABCSeries from pandas.core.dtypes.missing import is_valid_na_for_dtype from pandas.core.arrays.datetimes import ( DatetimeArray, tz_to_dtype, ) import pandas.core.common as com from pandas.core.indexes.base import ( Index, maybe_extract_name, ) from pandas.core.indexes.datetimelike import DatetimeTimedeltaMixin from pandas.core.indexes.extension import inherit_names from pandas.core.tools.times import to_time if TYPE_CHECKING: from collections.abc import Hashable from pandas._typing import ( Dtype, DtypeObj, Frequency, IntervalClosedType, TimeAmbiguous, TimeNonexistent, npt, TimeUnit, ) from pandas.core.api import ( DataFrame, PeriodIndex, ) from pandas._libs.tslibs.dtypes import OFFSET_TO_PERIOD_FREQSTR def _new_DatetimeIndex(cls, d): """ This is called upon unpickling, rather than the default which doesn't have arguments and breaks __new__ """ if "data" in d and not isinstance(d["data"], DatetimeIndex): # Avoid need to verify integrity by calling simple_new directly data = d.pop("data") if not isinstance(data, DatetimeArray): # For backward compat with older pickles, we may need to construct # a DatetimeArray to adapt to the newer _simple_new signature tz = d.pop("tz") freq = d.pop("freq") dta = DatetimeArray._simple_new(data, dtype=tz_to_dtype(tz), freq=freq) else: dta = data for key in ["tz", "freq"]: # These are already stored in our DatetimeArray; if they are # also in the pickle and don't match, we have a problem. if key in d: assert d[key] == getattr(dta, key) d.pop(key) result = cls._simple_new(dta, **d) else: with warnings.catch_warnings(): # TODO: If we knew what was going in to **d, we might be able to # go through _simple_new instead warnings.simplefilter("ignore") result = cls.__new__(cls, **d) return result @inherit_names( DatetimeArray._field_ops + [ method for method in DatetimeArray._datetimelike_methods if method not in ("tz_localize", "tz_convert", "strftime") ], DatetimeArray, wrap=True, ) @inherit_names(["is_normalized"], DatetimeArray, cache=True) @inherit_names( [ "tz", "tzinfo", "dtype", "to_pydatetime", "date", "time", "timetz", "std", *DatetimeArray._bool_ops, ], DatetimeArray, ) @set_module("pandas") class DatetimeIndex(DatetimeTimedeltaMixin): """ Immutable ndarray-like of datetime64 data. Represented internally as int64, and which can be boxed to Timestamp objects that are subclasses of datetime and carry metadata. .. versionchanged:: 2.0.0 The various numeric date/time attributes (:attr:`~DatetimeIndex.day`, :attr:`~DatetimeIndex.month`, :attr:`~DatetimeIndex.year` etc.) now have dtype ``int32``. Previously they had dtype ``int64``. Parameters ---------- data : array-like (1-dimensional) Datetime-like data to construct index with. freq : str or pandas offset object, optional One of pandas date offset strings or corresponding objects. The string 'infer' can be passed in order to set the frequency of the index as the inferred frequency upon creation. tz : zoneinfo.ZoneInfo, pytz.timezone, dateutil.tz.tzfile, datetime.tzinfo or str Set the Timezone of the data. ambiguous : 'infer', bool-ndarray, 'NaT', 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 a ValueError if there are ambiguous times. dayfirst : bool, default False If True, parse dates in `data` with the day first order. yearfirst : bool, default False If True parse dates in `data` with the year first order. dtype : numpy.dtype or DatetimeTZDtype or str, default None Note that the only NumPy dtype allowed is `datetime64[ns]`. copy : bool, default None Whether to copy input data, only relevant for array, Series, and Index inputs (for other input, e.g. a list, a new array is created anyway). Defaults to True for array input and False for Index/Series. Set to False to avoid copying array input at your own risk (if you know the input data won't be modified elsewhere). Set to True to force copying Series/Index up front. name : label, default None Name to be stored in the index. Attributes ---------- year month day hour minute second microsecond nanosecond date time timetz dayofyear day_of_year dayofweek day_of_week weekday quarter tz freq freqstr is_month_start is_month_end is_quarter_start is_quarter_end is_year_start is_year_end is_leap_year inferred_freq Methods ------- normalize strftime snap tz_convert tz_localize round floor ceil to_period to_pydatetime to_series to_frame to_julian_date month_name day_name mean std See Also -------- Index : The base pandas Index type. TimedeltaIndex : Index of timedelta64 data. PeriodIndex : Index of Period data. to_datetime : Convert argument to datetime. date_range : Create a fixed-frequency DatetimeIndex. Notes ----- To learn more about the frequency strings, please see :ref:`this link`. Examples -------- >>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"]) >>> idx DatetimeIndex(['2020-01-01 10:00:00+00:00', '2020-02-01 11:00:00+00:00'], dtype='datetime64[us, UTC]', freq=None) """ _typ = "datetimeindex" _data_cls = DatetimeArray _supports_partial_string_indexing = True @property def _engine_type(self) -> type[libindex.DatetimeEngine]: return libindex.DatetimeEngine _data: DatetimeArray _values: DatetimeArray tz: dt.tzinfo | None # -------------------------------------------------------------------- # methods that dispatch to DatetimeArray and wrap result def strftime(self, date_format) -> Index: """ Convert to Index using specified date_format. Return an Index of formatted strings specified by date_format, which supports the same string format as the python standard library. Details of the string format can be found in `python string format doc `__. Formats supported by the C `strftime` API but not by the python string format doc (such as `"%R"`, `"%r"`) are not officially supported and should be preferably replaced with their supported equivalents (such as `"%H:%M"`, `"%I:%M:%S %p"`). Note that `PeriodIndex` support additional directives, detailed in `Period.strftime`. Parameters ---------- date_format : str Date format string (e.g. "%Y-%m-%d"). Returns ------- ndarray[object] NumPy ndarray of formatted strings. See Also -------- to_datetime : Convert the given argument to datetime. DatetimeIndex.normalize : Return DatetimeIndex with times to midnight. DatetimeIndex.round : Round the DatetimeIndex to the specified freq. DatetimeIndex.floor : Floor the DatetimeIndex to the specified freq. Timestamp.strftime : Format a single Timestamp. Period.strftime : Format a single Period. Examples -------- >>> rng = pd.date_range(pd.Timestamp("2018-03-10 09:00"), periods=3, freq="s") >>> rng.strftime("%B %d, %Y, %r") Index(['March 10, 2018, 09:00:00 AM', 'March 10, 2018, 09:00:01 AM', 'March 10, 2018, 09:00:02 AM'], dtype='str') """ arr = self._data.strftime(date_format) return Index(arr, name=self.name, dtype=arr.dtype, copy=False) def tz_convert(self, tz) -> Self: """ Convert tz-aware Datetime Array/Index from one time zone to another. Parameters ---------- tz : str, zoneinfo.ZoneInfo, 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 Datetme Array/Index with target `tz`. 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[us, 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[us, 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[us, 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[us]', freq='h') """ # noqa: E501 arr = self._data.tz_convert(tz) return type(self)._simple_new(arr, name=self.name, refs=self._references) 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, zoneinfo.ZoneInfo,, 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 a ValueError 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 a ValueError 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[us]', 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[us, 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[us]', 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[us, 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[us, 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'], dtype="M8[ns]")) >>> 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] """ # noqa: E501 arr = self._data.tz_localize(tz, ambiguous, nonexistent) return type(self)._simple_new(arr, name=self.name) def to_period(self, freq=None) -> PeriodIndex: """ 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 a Period object. Will be inferred by default. Returns ------- PeriodArray/PeriodIndex Immutable ndarray holding ordinal values at a particular frequency. 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.indexes.api import PeriodIndex arr = self._data.to_period(freq) return PeriodIndex._simple_new(arr, name=self.name) def to_julian_date(self) -> Index: """ Convert TimeStamp to a Julian Date. This method returns the number of days as a float since noon January 1, 4713 BC. https://en.wikipedia.org/wiki/Julian_day Returns ------- ndarray or Index Float values that represent each date in Julian Calendar. See Also -------- Timestamp.to_julian_date : Equivalent method on ``Timestamp`` objects. Examples -------- >>> idx = pd.DatetimeIndex(["2028-08-12 00:54", "2028-08-12 02:06"]) >>> idx.to_julian_date() Index([2461995.5375, 2461995.5875], dtype='float64') """ arr = self._data.to_julian_date() return Index._simple_new(arr, name=self.name) 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 """ df = self._data.isocalendar() return df.set_index(self) @cache_readonly def _resolution_obj(self) -> Resolution: return self._data._resolution_obj # -------------------------------------------------------------------- # Constructors def __new__( cls, data=None, freq: Frequency | lib.NoDefault = lib.no_default, tz=lib.no_default, ambiguous: TimeAmbiguous = "raise", dayfirst: bool = False, yearfirst: bool = False, dtype: Dtype | None = None, copy: bool | None = None, name: Hashable | None = None, ) -> Self: if is_scalar(data): cls._raise_scalar_data_error(data) # - Cases checked above all return/raise before reaching here - # name = maybe_extract_name(name, data, cls) # GH#63388 data, copy = cls._maybe_copy_array_input(data, copy, dtype) if ( isinstance(data, DatetimeArray) and freq is lib.no_default and tz is lib.no_default and dtype is None ): # fastpath, similar logic in TimedeltaIndex.__new__; # Note in this particular case we retain non-nano. if copy: data = data.copy() return cls._simple_new(data, name=name) dtarr = DatetimeArray._from_sequence_not_strict( data, dtype=dtype, copy=copy, tz=tz, freq=freq, dayfirst=dayfirst, yearfirst=yearfirst, ambiguous=ambiguous, ) refs = None if not copy and isinstance(data, (Index, ABCSeries)): refs = data._references subarr = cls._simple_new(dtarr, name=name, refs=refs) return subarr # -------------------------------------------------------------------- @cache_readonly def _is_dates_only(self) -> bool: """ Return a boolean if we are only dates (and don't have a timezone) Returns ------- bool """ if isinstance(self.freq, Tick): delta = Timedelta(self.freq) if delta % dt.timedelta(days=1) != dt.timedelta(days=0): return False return self._values._is_dates_only def __reduce__(self): d = {"data": self._data, "name": self.name} return _new_DatetimeIndex, (type(self), d), None def _is_comparable_dtype(self, dtype: DtypeObj) -> bool: """ Can we compare values of the given dtype to our own? """ if isinstance(dtype, ArrowDtype): # GH#62277 if dtype.kind != "M": return False pa_dtype = dtype.pyarrow_dtype if (pa_dtype.tz is None) ^ (self.tz is None): return False return True if self.tz is not None: # If we have tz, we can compare to tzaware return isinstance(dtype, DatetimeTZDtype) # if we dont have tz, we can only compare to tznaive return lib.is_np_dtype(dtype, "M") # -------------------------------------------------------------------- # Rendering Methods @cache_readonly def _formatter_func(self): # Note this is equivalent to the DatetimeIndexOpsMixin method but # uses the maybe-cached self._is_dates_only instead of re-computing it. from pandas.io.formats.format import get_format_datetime64 formatter = get_format_datetime64(is_dates_only=self._is_dates_only) return lambda x: f"'{formatter(x)}'" # -------------------------------------------------------------------- # Set Operation Methods def _can_range_setop(self, other) -> bool: # GH 46702: If self or other have non-UTC tzs, DST transitions prevent # range representation due to no singular step if ( self.tz is not None and not timezones.is_utc(self.tz) and not timezones.is_fixed_offset(self.tz) ): return False if ( other.tz is not None and not timezones.is_utc(other.tz) and not timezones.is_fixed_offset(other.tz) ): return False return super()._can_range_setop(other) # -------------------------------------------------------------------- def _get_time_micros(self) -> npt.NDArray[np.int64]: """ Return the number of microseconds since midnight. Returns ------- ndarray[int64_t] """ values = self._data._local_timestamps() ppd = periods_per_day(self._data._creso) frac = values % ppd if self.unit == "ns": micros = frac // 1000 elif self.unit == "us": micros = frac elif self.unit == "ms": micros = frac * 1000 elif self.unit == "s": micros = frac * 1_000_000 else: # pragma: no cover raise NotImplementedError(self.unit) micros[self._isnan] = -1 return micros def snap(self, freq: Frequency = "S") -> DatetimeIndex: """ Snap time stamps to nearest occurring frequency. Parameters ---------- freq : str, Timedelta, datetime.timedelta, or DateOffset, default 'S' Frequency strings can have multiples, e.g. '5h'. See :ref:`here ` for a list of frequency aliases. Returns ------- DatetimeIndex Time stamps to nearest occurring `freq`. See Also -------- DatetimeIndex.round : Perform round operation on the data to the specified `freq`. DatetimeIndex.floor : Perform floor operation on the data to the specified `freq`. Examples -------- >>> idx = pd.DatetimeIndex( ... ["2023-01-01", "2023-01-02", "2023-02-01", "2023-02-02"], ... dtype="M8[ns]", ... ) >>> idx DatetimeIndex(['2023-01-01', '2023-01-02', '2023-02-01', '2023-02-02'], dtype='datetime64[ns]', freq=None) >>> idx.snap("MS") DatetimeIndex(['2023-01-01', '2023-01-01', '2023-02-01', '2023-02-01'], dtype='datetime64[ns]', freq=None) """ # Superdumb, punting on any optimizing freq = to_offset(freq) dta = self._data.copy() for i, v in enumerate(self): s = v if not freq.is_on_offset(s): t0 = freq.rollback(s) t1 = freq.rollforward(s) if abs(s - t0) < abs(t1 - s): s = t0 else: s = t1 dta[i] = s return DatetimeIndex._simple_new(dta, name=self.name) # -------------------------------------------------------------------- # Indexing Methods def _parsed_string_to_bounds( self, reso: Resolution, parsed: dt.datetime ) -> tuple[Timestamp, Timestamp]: """ Calculate datetime bounds for parsed time string and its resolution. Parameters ---------- reso : Resolution Resolution provided by parsed string. parsed : datetime Datetime from parsed string. Returns ------- lower, upper: pd.Timestamp """ freq = OFFSET_TO_PERIOD_FREQSTR.get(reso.attr_abbrev, reso.attr_abbrev) per = Period(parsed, freq=freq) start = per.start_time # Can't use end_time here bc that will subtract a microsecond # instead of a nanosecond end = (per + 1).start_time - np.timedelta64(1, "ns") start = start.as_unit(self.unit) end = end.as_unit(self.unit) # GH 24076 # If an incoming date string contained a UTC offset, need to localize # the parsed date to this offset first before aligning with the index's # timezone start = start.tz_localize(parsed.tzinfo) end = end.tz_localize(parsed.tzinfo) if parsed.tzinfo is not None: if self.tz is None: raise ValueError( "The index must be timezone aware when indexing " "with a date string with a UTC offset" ) # The flipped case with parsed.tz is None and self.tz is not None # is ruled out bc parsed and reso are produced by _parse_with_reso, # which localizes parsed. return start, end def _parse_with_reso(self, label: str) -> tuple[Timestamp, Resolution]: parsed, reso = super()._parse_with_reso(label) parsed = Timestamp(parsed) if self.tz is not None and parsed.tzinfo is None: # we special-case timezone-naive strings and timezone-aware # DatetimeIndex # https://github.com/pandas-dev/pandas/pull/36148#issuecomment-687883081 parsed = parsed.tz_localize(self.tz) return parsed, reso def _disallow_mismatched_indexing(self, key) -> None: """ Check for mismatched-tzawareness indexing and re-raise as KeyError. """ # we get here with isinstance(key, self._data._recognized_scalars) try: # GH#36148 self._data._assert_tzawareness_compat(key) except TypeError as err: raise KeyError(key) from err def get_loc(self, key): """ Get integer location for requested label Returns ------- loc : int """ self._check_indexing_error(key) orig_key = key if is_valid_na_for_dtype(key, self.dtype): key = NaT if isinstance(key, self._data._recognized_scalars): # needed to localize naive datetimes self._disallow_mismatched_indexing(key) key = Timestamp(key) elif isinstance(key, str): try: parsed, reso = self._parse_with_reso(key) except ValueError as err: raise KeyError(key) from err self._disallow_mismatched_indexing(parsed) if self._can_partial_date_slice(reso): try: return self._partial_date_slice(reso, parsed) except KeyError as err: raise KeyError(key) from err key = parsed elif isinstance(key, dt.timedelta): # GH#20464 raise TypeError( f"Cannot index {type(self).__name__} with {type(key).__name__}" ) elif isinstance(key, dt.time): return self.indexer_at_time(key) else: # unrecognized type raise KeyError(key) try: return Index.get_loc(self, key) except KeyError as err: raise KeyError(orig_key) from err def _maybe_cast_slice_bound(self, label, side: str): """ This function should be overloaded in subclasses that allow non-trivial casting on label-slice bounds, e.g. datetime-like indices allowing strings containing formatted datetimes. Parameters ---------- label : object side : {'left', 'right'} Returns ------- label : object Notes ----- Value of `side` parameter should be validated in caller. """ # GH#42855 handle date here instead of get_slice_bound if isinstance(label, dt.date) and not isinstance(label, dt.datetime): # Pandas supports slicing with dates, treated as datetimes at midnight. # https://github.com/pandas-dev/pandas/issues/31501 label = Timestamp(label).to_pydatetime() warnings.warn( # GH#35830 deprecate last remaining inconsistent date treatment "Slicing with a datetime.date object is deprecated. " "Explicitly cast to Timestamp instead.", Pandas4Warning, stacklevel=find_stack_level(), ) label = super()._maybe_cast_slice_bound(label, side) self._data._assert_tzawareness_compat(label) return Timestamp(label) def slice_indexer(self, start=None, end=None, step=None): """ Return indexer for specified label slice. Index.slice_indexer, customized to handle time slicing. In addition to functionality provided by Index.slice_indexer, does the following: - if both `start` and `end` are instances of `datetime.time`, it invokes `indexer_between_time` - if `start` and `end` are both either string or None perform value-based selection in non-monotonic cases. """ # For historical reasons DatetimeIndex supports slices between two # instances of datetime.time as if it were applying a slice mask to # an array of (self.hour, self.minute, self.seconds, self.microsecond). if isinstance(start, dt.time) and isinstance(end, dt.time): if step is not None and step != 1: raise ValueError("Must have step size of 1 with time slices") return self.indexer_between_time(start, end) if isinstance(start, dt.time) or isinstance(end, dt.time): raise KeyError("Cannot mix time and non-time slice keys") def check_str_or_none(point) -> bool: return point is not None and not isinstance(point, str) # GH#33146 if start and end are combinations of str and None and Index is not # monotonic, we can not use Index.slice_indexer because it does not honor the # actual elements, is only searching for start and end if ( check_str_or_none(start) or check_str_or_none(end) or self.is_monotonic_increasing ): return Index.slice_indexer(self, start, end, step) mask = np.array(True) in_index = True if start is not None: start_casted = self._maybe_cast_slice_bound(start, "left") mask = start_casted <= self in_index &= (start_casted == self).any() if end is not None: end_casted = self._maybe_cast_slice_bound(end, "right") mask = (self <= end_casted) & mask in_index &= (end_casted == self).any() if not in_index: raise KeyError( "Value based partial slicing on non-monotonic DatetimeIndexes " "with non-existing keys is not allowed.", ) indexer = mask.nonzero()[0][::step] if len(indexer) == len(self): return slice(None) else: return indexer # -------------------------------------------------------------------- @property def inferred_type(self) -> str: # b/c datetime is represented as microseconds since the epoch, make # sure we can't have ambiguous indexing return "datetime64" def indexer_at_time(self, time, asof: bool = False) -> npt.NDArray[np.intp]: """ Return index locations of values at particular time of day. Parameters ---------- time : datetime.time or str Time passed in either as object (datetime.time) or as string in appropriate format ("%H:%M", "%H%M", "%I:%M%p", "%I%M%p", "%H:%M:%S", "%H%M%S", "%I:%M:%S%p", "%I%M%S%p"). asof : bool, default False This parameter is currently not supported. Returns ------- np.ndarray[np.intp] Index locations of values at given `time` of day. See Also -------- indexer_between_time : Get index locations of values between particular times of day. DataFrame.at_time : Select values at particular time of day. Examples -------- >>> idx = pd.DatetimeIndex( ... ["1/1/2020 10:00", "2/1/2020 11:00", "3/1/2020 10:00"] ... ) >>> idx.indexer_at_time("10:00") array([0, 2]) """ if asof: raise NotImplementedError("'asof' argument is not supported") if isinstance(time, str): from dateutil.parser import parse orig = time try: alt = to_time(time) except ValueError: warnings.warn( # GH#50839 f"The string '{orig}' cannot be parsed using pd.core.tools.to_time " f"and in a future version will raise. " "Use an unambiguous time string format or explicitly cast to " "`datetime.time` before calling.", Pandas4Warning, stacklevel=find_stack_level(), ) time = parse(time).time() else: try: time = parse(time).time() except ValueError: # e.g. '23550' raises dateutil.parser._parser.ParserError time = alt if alt != time: warnings.warn( # GH#50839 f"The string '{orig}' is currently parsed as {time} " f"but in a future version will be parsed as {alt}, consistent" "with `between_time` behavior. To avoid this warning, " "use an unambiguous string format or explicitly cast to " "`datetime.time` before calling.", Pandas4Warning, stacklevel=find_stack_level(), ) if time.tzinfo: if self.tz is None: raise ValueError("Index must be timezone aware.") time_micros = self.tz_convert(time.tzinfo)._get_time_micros() else: time_micros = self._get_time_micros() micros = _time_to_micros(time) return (time_micros == micros).nonzero()[0] def indexer_between_time( self, start_time, end_time, include_start: bool = True, include_end: bool = True ) -> npt.NDArray[np.intp]: """ Return index locations of values between particular times of day. Parameters ---------- start_time, end_time : datetime.time, str Time passed either as object (datetime.time) or as string in appropriate format ("%H:%M", "%H%M", "%I:%M%p", "%I%M%p", "%H:%M:%S", "%H%M%S", "%I:%M:%S%p","%I%M%S%p"). include_start : bool, default True Include boundaries; whether to set start bound as closed or open. include_end : bool, default True Include boundaries; whether to set end bound as closed or open. Returns ------- np.ndarray[np.intp] Index locations of values between particular times of day. See Also -------- indexer_at_time : Get index locations of values at particular time of day. DataFrame.between_time : Select values between particular times of day. Examples -------- >>> idx = pd.date_range("2023-01-01", periods=4, freq="h") >>> idx DatetimeIndex(['2023-01-01 00:00:00', '2023-01-01 01:00:00', '2023-01-01 02:00:00', '2023-01-01 03:00:00'], dtype='datetime64[us]', freq='h') >>> idx.indexer_between_time("00:00", "2:00", include_end=False) array([0, 1]) """ start_time = to_time(start_time) end_time = to_time(end_time) time_micros = self._get_time_micros() start_micros = _time_to_micros(start_time) end_micros = _time_to_micros(end_time) if include_start and include_end: lop = rop = operator.le elif include_start: lop = operator.le rop = operator.lt elif include_end: lop = operator.lt rop = operator.le else: lop = rop = operator.lt if start_time <= end_time: join_op = operator.and_ else: join_op = operator.or_ mask = join_op(lop(start_micros, time_micros), rop(time_micros, end_micros)) return mask.nonzero()[0] @set_module("pandas") def date_range( start=None, end=None, periods=None, freq=None, tz=None, normalize: bool = False, name: Hashable | None = None, inclusive: IntervalClosedType = "both", *, unit: TimeUnit | None = None, **kwargs, ) -> DatetimeIndex: """ Return a fixed frequency DatetimeIndex. Returns the range of equally spaced time points (where the difference between any two adjacent points is specified by the given frequency) such that they fall in the range `[start, end]` , where the first one and the last one are, resp., the first and last time points in that range that fall on the boundary of ``freq`` (if given as a frequency string) or that are valid for ``freq`` (if given as a :class:`pandas.tseries.offsets.DateOffset`). If ``freq`` is positive, the points satisfy `start <[=] x <[=] end`, and if ``freq`` is negative, the points satisfy `end <[=] x <[=] start`. (If exactly one of ``start``, ``end``, or ``freq`` is *not* specified, this missing parameter can be computed given ``periods``, the number of timesteps in the range. See the note below.) Parameters ---------- start : str or datetime-like, optional Left bound for generating dates. end : str or datetime-like, optional Right bound for generating dates. periods : int, optional Number of periods to generate. freq : str, Timedelta, datetime.timedelta, or DateOffset, default 'D' Frequency strings can have multiples, e.g. '5h'. See :ref:`here ` for a list of frequency aliases. tz : str or tzinfo, optional Time zone name for returning localized DatetimeIndex, for example 'Asia/Hong_Kong'. By default, the resulting DatetimeIndex is timezone-naive unless timezone-aware datetime-likes are passed. normalize : bool, default False Normalize start/end dates to midnight before generating date range. name : Hashable, default None Name of the resulting DatetimeIndex. inclusive : {"both", "neither", "left", "right"}, default "both" Include boundaries; Whether to set each bound as closed or open. unit : {'s', 'ms', 'us', 'ns', None}, default None Specify the desired resolution of the result. If not specified, this is inferred from the 'start', 'end', and 'freq' using the same inference as :class:`Timestamp` taking the highest resolution of the three that are provided. .. versionadded:: 2.0.0 **kwargs For compatibility. Has no effect on the result. Returns ------- DatetimeIndex A DatetimeIndex object of the generated dates. See Also -------- DatetimeIndex : An immutable container for datetimes. timedelta_range : Return a fixed frequency TimedeltaIndex. period_range : Return a fixed frequency PeriodIndex. interval_range : Return a fixed frequency IntervalIndex. Notes ----- Of the four parameters ``start``, ``end``, ``periods``, and ``freq``, a maximum of three can be specified at once. Of the three parameters ``start``, ``end``, and ``periods``, at least two must be specified. If ``freq`` is omitted, the resulting ``DatetimeIndex`` will have ``periods`` linearly spaced elements between ``start`` and ``end`` (closed on both sides). To learn more about the frequency strings, please see :ref:`this link`. Examples -------- **Specifying the values** The next four examples generate the same `DatetimeIndex`, but vary the combination of `start`, `end` and `periods`. Specify `start` and `end`, with the default daily frequency. >>> pd.date_range(start="1/1/2018", end="1/08/2018") DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04', '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08'], dtype='datetime64[us]', freq='D') Specify timezone-aware `start` and `end`, with the default daily frequency. >>> pd.date_range( ... start=pd.to_datetime("1/1/2018").tz_localize("Europe/Berlin"), ... end=pd.to_datetime("1/08/2018").tz_localize("Europe/Berlin"), ... ) DatetimeIndex(['2018-01-01 00:00:00+01:00', '2018-01-02 00:00:00+01:00', '2018-01-03 00:00:00+01:00', '2018-01-04 00:00:00+01:00', '2018-01-05 00:00:00+01:00', '2018-01-06 00:00:00+01:00', '2018-01-07 00:00:00+01:00', '2018-01-08 00:00:00+01:00'], dtype='datetime64[us, Europe/Berlin]', freq='D') Specify `start` and `periods`, the number of periods (days). >>> pd.date_range(start="1/1/2018", periods=8) DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04', '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08'], dtype='datetime64[us]', freq='D') Specify `end` and `periods`, the number of periods (days). >>> pd.date_range(end="1/1/2018", periods=8) DatetimeIndex(['2017-12-25', '2017-12-26', '2017-12-27', '2017-12-28', '2017-12-29', '2017-12-30', '2017-12-31', '2018-01-01'], dtype='datetime64[us]', freq='D') Specify `start`, `end`, and `periods`; the frequency is generated automatically (linearly spaced). >>> pd.date_range(start="2018-04-24", end="2018-04-27", periods=3) DatetimeIndex(['2018-04-24 00:00:00', '2018-04-25 12:00:00', '2018-04-27 00:00:00'], dtype='datetime64[us]', freq=None) **Other Parameters** Changed the `freq` (frequency) to ``'ME'`` (month end frequency). >>> pd.date_range(start="1/1/2018", periods=5, freq="ME") DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31', '2018-04-30', '2018-05-31'], dtype='datetime64[us]', freq='ME') Multiples are allowed >>> pd.date_range(start="1/1/2018", periods=5, freq="3ME") DatetimeIndex(['2018-01-31', '2018-04-30', '2018-07-31', '2018-10-31', '2019-01-31'], dtype='datetime64[us]', freq='3ME') `freq` can also be specified as an Offset object. >>> pd.date_range(start="1/1/2018", periods=5, freq=pd.offsets.MonthEnd(3)) DatetimeIndex(['2018-01-31', '2018-04-30', '2018-07-31', '2018-10-31', '2019-01-31'], dtype='datetime64[us]', freq='3ME') Specify `tz` to set the timezone. >>> pd.date_range(start="1/1/2018", periods=5, tz="Asia/Tokyo") DatetimeIndex(['2018-01-01 00:00:00+09:00', '2018-01-02 00:00:00+09:00', '2018-01-03 00:00:00+09:00', '2018-01-04 00:00:00+09:00', '2018-01-05 00:00:00+09:00'], dtype='datetime64[us, Asia/Tokyo]', freq='D') `inclusive` controls whether to include `start` and `end` that are on the boundary. The default, "both", includes boundary points on either end. >>> pd.date_range(start="2017-01-01", end="2017-01-04", inclusive="both") DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04'], dtype='datetime64[us]', freq='D') Use ``inclusive='left'`` to exclude `end` if it falls on the boundary. >>> pd.date_range(start="2017-01-01", end="2017-01-04", inclusive="left") DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03'], dtype='datetime64[us]', freq='D') Use ``inclusive='right'`` to exclude `start` if it falls on the boundary, and similarly ``inclusive='neither'`` will exclude both `start` and `end`. >>> pd.date_range(start="2017-01-01", end="2017-01-04", inclusive="right") DatetimeIndex(['2017-01-02', '2017-01-03', '2017-01-04'], dtype='datetime64[us]', freq='D') **Specify a unit** >>> pd.date_range(start="2017-01-01", periods=10, freq="100YS", unit="s") DatetimeIndex(['2017-01-01', '2117-01-01', '2217-01-01', '2317-01-01', '2417-01-01', '2517-01-01', '2617-01-01', '2717-01-01', '2817-01-01', '2917-01-01'], dtype='datetime64[s]', freq='100YS-JAN') """ if freq is None and com.any_none(periods, start, end): freq = "D" if freq is not None: freq = to_offset(freq) if start is NaT or end is NaT: # This check needs to come before the `unit = start.unit` line below raise ValueError("Neither `start` nor `end` can be NaT") if unit is None: # Infer the unit based on the inputs if start is not None and end is not None: start = Timestamp(start) end = Timestamp(end) if abbrev_to_npy_unit(start.unit) > abbrev_to_npy_unit(end.unit): unit = start.unit else: unit = end.unit elif start is not None: start = Timestamp(start) unit = start.unit elif end is not None: end = Timestamp(end) unit = end.unit else: raise ValueError( "Of the four parameters: start, end, periods, " "and freq, exactly three must be specified" ) # Last we need to watch out for cases where the 'freq' implies a higher # unit than either start or end if freq is not None: creso = abbrev_to_npy_unit(unit) if isinstance(freq, Tick): if freq._creso > creso: unit = freq.base.freqstr # type: ignore[assignment] elif hasattr(freq, "offset") and freq.offset is not None: # e.g. BDay with an offset td = Timedelta(freq.offset) if abbrev_to_npy_unit(td.unit) > creso: unit = td.unit elif type(freq) is DateOffset: if getattr(freq, "nanoseconds", 0) != 0: # e.g. test_freq_dateoffset_with_relateivedelta_nanos unit = "ns" elif getattr(freq, "microseconds", 0) != 0 and unit != "ns": unit = "us" elif getattr(freq, "milliseconds", 0) != 0 and unit not in ["ns", "us"]: unit = "ms" dtarr = DatetimeArray._generate_range( start=start, end=end, periods=periods, freq=freq, tz=tz, normalize=normalize, inclusive=inclusive, unit=unit, **kwargs, ) return DatetimeIndex._simple_new(dtarr, name=name) @set_module("pandas") def bdate_range( start=None, end=None, periods: int | None = None, freq: Frequency | dt.timedelta = "B", tz=None, normalize: bool = True, name: Hashable | None = None, weekmask=None, holidays=None, inclusive: IntervalClosedType = "both", **kwargs, ) -> DatetimeIndex: """ Return a fixed frequency DatetimeIndex with business day as the default. Parameters ---------- start : str or datetime-like, default None Left bound for generating dates. end : str or datetime-like, default None Right bound for generating dates. periods : int, default None Number of periods to generate. freq : str, Timedelta, datetime.timedelta, or DateOffset, default 'B' Frequency strings can have multiples, e.g. '5h'. The default is business daily ('B'). tz : str or None Time zone name for returning localized DatetimeIndex, for example Asia/Beijing. normalize : bool, default False Normalize start/end dates to midnight before generating date range. name : Hashable, default None Name of the resulting DatetimeIndex. weekmask : str or None, default None Weekmask of valid business days, passed to ``numpy.busdaycalendar``, only used when custom frequency strings are passed. The default value None is equivalent to 'Mon Tue Wed Thu Fri'. holidays : list-like or None, default None Dates to exclude from the set of valid business days, passed to ``numpy.busdaycalendar``, only used when custom frequency strings are passed. inclusive : {"both", "neither", "left", "right"}, default "both" Include boundaries; Whether to set each bound as closed or open. **kwargs For compatibility. Has no effect on the result. Returns ------- DatetimeIndex Fixed frequency DatetimeIndex. See Also -------- date_range : Return a fixed frequency DatetimeIndex. period_range : Return a fixed frequency PeriodIndex. timedelta_range : Return a fixed frequency TimedeltaIndex. Notes ----- Of the four parameters: ``start``, ``end``, ``periods``, and ``freq``, exactly three must be specified. Specifying ``freq`` is a requirement for ``bdate_range``. Use ``date_range`` if specifying ``freq`` is not desired. To learn more about the frequency strings, please see :ref:`this link`. Examples -------- Note how the two weekend days are skipped in the result. >>> pd.bdate_range(start="1/1/2018", end="1/08/2018") DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04', '2018-01-05', '2018-01-08'], dtype='datetime64[us]', freq='B') """ if freq is None: msg = "freq must be specified for bdate_range; use date_range instead" raise TypeError(msg) if isinstance(freq, str) and freq.upper().startswith("C"): msg = f"invalid custom frequency string: {freq}" if freq == "CBH": raise ValueError(f"{msg}, did you mean cbh?") try: weekmask = weekmask or "Mon Tue Wed Thu Fri" freq = prefix_mapping[freq](holidays=holidays, weekmask=weekmask) except (KeyError, TypeError) as err: raise ValueError(msg) from err elif holidays or weekmask: msg = ( "a custom frequency string is required when holidays or " f"weekmask are passed, got frequency {freq}" ) raise ValueError(msg) return date_range( start=start, end=end, periods=periods, freq=freq, tz=tz, normalize=normalize, name=name, inclusive=inclusive, **kwargs, ) def _time_to_micros(time_obj: dt.time) -> int: seconds = time_obj.hour * 60 * 60 + 60 * time_obj.minute + time_obj.second return 1_000_000 * seconds + time_obj.microsecond