Pi 8ddlmZddlmZmZmZddlmZmZmZm Z m Z ddl Z ddl Z ddlmZddlmZddlmZmZddlmZmZmZmZmZmZmZmZmZmZm Z m!Z!m"Z"m#Z#m$Z$m%Z%m&Z&m'Z'm(Z(dd l)m*Z*dd l+m,Z,dd l-m.Z.dd l/m0Z0dd l1m2Z2ddl3m4Z4m5Z5m6Z6m7Z7m8Z8m9Z9ddl:m;Z;mm?Z?ddl@mAZBddlCmDZDddlEmFcmGZHddlImJZJddlKmLZLmMZMerFddlNmOZOmPZPmQZQddlRmSZSmTZTmUZUmVZVmWZWmXZXmYZYmZZZddl[m\Z\m]Z]ddl@m^Z^ededZ_ededZ`dZae d`dad#Zbe d`dbd&Zb dcddd*Zbdedfd0Zce.d1Gd2d3eBjdeBjeZfd4dd4d4d5dd6dgdAZgdhdDZh didjdIZidedkdJZjdldLZkdMZl dmdndOZmdodSZndpdVZodqdYZpdrd_ZqdS)s) annotations)datetime timedeltatzinfo) TYPE_CHECKINGSelfTypeVarcastoverloadN)using_string_dtype) get_option)libtslib) BaseOffsetNaTNaTType Resolution Timestampastype_overflowsafefieldsget_resolutionget_supported_dtypeget_unit_from_dtypeints_to_pydatetimeis_date_array_normalizedis_supported_dtype is_unitlessnormalize_i8_timestamps timezones to_offsettz_convert_from_utc tzconversion)abbrev_to_npy_unit)PerformanceWarning) set_module)find_stack_level)validate_inclusive) DT64NS_DTYPE INT64_DTYPE is_bool_dtypeis_float_dtypeis_string_dtype pandas_dtype)DatetimeTZDtypeExtensionDtype PeriodDtype)isna) datetimelike)generate_regular_range)get_period_alias)DayTick)Callable GeneratorIterator) ArrayLikeDateTimeErrorChoicesDtypeObjIntervalClosedType TimeAmbiguousTimeNonexistentTimeUnitnpt) DataFrame Timedelta PeriodArray_TimestampNoneT1_TimestampNoneT2i'.tzrunitr@returnr.cdSNrHrIs p/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/pandas/core/arrays/datetimes.py tz_to_dtyperPmsFIcNonenp.dtype[np.datetime64]cdSrLrMrNs rOrPrPqsLOCrQns tzinfo | None)np.dtype[np.datetime64] | DatetimeTZDtypecX|tjd|dSt||S)z Return a datetime64[ns] dtype appropriate for the given timezone. Parameters ---------- tz : tzinfo or None unit : str, default "ns" Returns ------- np.dtype or Datetime64TZDType NM8[]rN)npdtyper.rNs rOrPrPus4 zx d &&&"40000rQnamestrfield docstring str | NonecHfd}||_||_t|S)Nc|}|jvrdrg|j}d}|r1|j}|d|d|}||j}nd}tj||||j }ntj ||j }|Stj ||j }| |dd}|S)N)startend startingMonthmonthresofloat64) fill_valueconvert) _local_timestamps _bool_opsendswithfreqkwdsgetr]rget_start_end_field_cresoget_date_field_maybe_mask_results)selfvaluesrqmonth_kwrr freq_nameresultr_s rOfz_field_accessor..fs '')) DN " "~~.// PyV9D#xx'89T9TUUH# $ II $I3E9hT[ .vu4;OOOM&vu4;GGG))&T9)UU rQ)__name____doc__property)r]r_r`r}s ` rO_field_accessorrs8>AJAI A;;rQz pandas.arraysc eZdZUdZdZeejfZdZ de d<dZ e dd Z e dd Zgd Zd e d<gdZd e d<gdZd e d<eezezgdzZd e d<gdZd e d<dZde d<dZde d<edZedefdfd# Zedd$d%dd(Zedd$ejejd$d$d)d*dd0Ze dd2d3dd=Zdd>Zdd@Z ddBZ!ddDZ"e ddEZ#e ddGZ$e$j%dHZ$e ddIZ&e ddJZ'e ddLZ(ddfdN Z)ddPZ*ddfdR Z+dSddTddXZ,ddYZ-dd\Z.dd^Z/dd_Z0e1j2 ddd`Z3ddaZ4ddbZ5ddddZ6dddeZ7dddfZ8e ddgZ9e ddhZ:e ddiZ;ddkZ<e=dldmdnZ>e=dodpdqZ?e=drdsdtZ@e=dudvdwZAe=dxdydzZBe=d{d|d}ZCe=d~ddZDe=dd2dZEdZFe=ddeFZGeGZHeGZIe=dddZJeJZKe=dddZLe=dddZMeMZNdZOe=ddeOPdZQe=ddeOPdZRe=dddZSe=dddZTe=dddZUe=dddZVe=dddZWddZXdQd$ddʈfdZY dddZZxZ[S) DatetimeArraya 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 ---------- data : 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 See Also -------- DatetimeIndex : Immutable Index for datetime-like data. Series : One-dimensional labeled array capable of holding datetime-like data. Timestamp : Pandas replacement for python datetime.datetime object. to_datetime : Convert argument to datetime. period_range : Return a fixed frequency PeriodIndex. 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[us] datetimearraycVtj|dpt|tSNM)r is_np_dtype isinstancer.)xs rOzDatetimeArray.s/ 3BBB( A ' 'rQzCallable[[DtypeObj], bool]_is_recognized_dtype)r datetime64daterJ np.datetime64c6tjd|jS)Nr)r[rrIrxs rO_internal_fill_valuez"DatetimeArray._internal_fill_values}UDI...rQtype[Timestamp]ctSrL)rrs rO _scalar_typezDatetimeArray._scalar_typesrQ)is_month_start is_month_endis_quarter_startis_quarter_end is_year_start is_year_end is_leap_yearz list[str]ro)yearrhdayhourminutesecondweekday dayofweek day_of_week dayofyear day_of_yearquarter days_in_month daysinmonth microsecond nanosecond _field_ops)rtimetimetz _other_ops)rIrqrH_datetimelike_ops) to_period tz_localize tz_convert normalizestrftimeroundfloorceil month_nameday_nameas_unit_datetimelike_methodsirW_dtypeNBaseOffset | None_freqc(t|}t|jt|tjr|j|krt dn9tj|jd}||jkrt d|S)Nz'Values resolution does not match dtype.r)_validate_dt64_dtyper\rr[ ValueError datetime_datarI)clsryr\vunits rO_validate_dtypezDatetimeArray._validate_dtype-s%U++V\*** eRX & & L|u$$ !JKKK%$V\2215E "" !JKKK rQrynpt.NDArray[np.datetime64]rqr\rc\t|tjsJ|jdksJt|tjr||jksJt |rJn|jt|jksJt ||}||_ |Sr) rr[ndarraykindr\rrursuper _simple_newr)rryrqr\r| __class__s rOrzDatetimeArray._simple_new<s&"*-----zS    eRX & & EFL(((("5)) ) )) )<#6v|#D#DDDDD$$VU33  rQFr\copyrboolc2||||SNr)_from_sequence_not_strict)rscalarsr\rs rO_from_sequencezDatetimeArray._from_sequenceQs,,WE,MMMrQraise)r\rrHrqdayfirst yearfirst ambiguous'str | BaseOffset | lib.NoDefault | Nonerrrr>c |du} |tjurd}ntj|}t |}t ||| }d} |t j|} t j||d\}}d} t|tr|j } t||||||| \} }t ||| || rtdtj| jd} t#d| } t%|| }|| | |}| | |jkr|| }d |i}||||S) z\ A non-strict version of _from_sequence, called from DatetimeIndex.__new__. Nr)cls_namerrHrrrout_unitz^Passed data is timezone-aware, incompatible with 'tz=None'. Use obj.tz_localize(None) instead.rr@rqr\r)r no_defaultr maybe_get_tzr_validate_tz_from_dtypedtl dtype_to_unit!ensure_arraylike_for_datetimelikerrrq_sequence_to_dt64rr[rr\r rPrrIr_maybe_pin_freq)rdatar\rrHrqrrrexplicit_tz_nonerI inferred_freqsubarr data_unit data_dtyper| validate_kwdss rOrz'DatetimeArray._from_sequence_not_strictUs&:   BB'++B$U++ $UB0@ A A  $U++D: $   d dM * * & IM&     r+;<<< >.>5  $V\2215 Y//  Y// m:NN   3 3 ^^D))F$i0 t]333 rQbothrUrIperiods int | Noner nonexistentr? inclusiver=rIr@c tj|}|+td|||fDrtdt j||||dkrtdt |}|t|}|t|}|tus |turtd| | dvrtdnd} || | d }|| | d }t| \} } t|||\}}t|||}|&t|||||}t|||||}|Ct|ts<||j|d}||j|d}t|tt$frt'||||| } n>t)||||| }t+jd |Dt*j} ||jn|j}|p|nt1j|s(t5| }t7j| ||||} |||||}|||||}nbt;t<|}t+jd|j |j z |d|j z} | j!dkr| "d} ||kr| s | s | dd} nzt|j }t|j }| r| sN| s%tG| r| d|kr | dd} | s%tG| r| d|kr | dd} | $d| d}tK|| }|&|||S)Nc3K|]}|duV dSrLrM.0rs rO z0DatetimeArray._generate_range..s&IIaT IIIIIIrQz1Must provide freq argument if no data is suppliedzVOf the four parameters: start, end, periods, and freq, exactly three must be specifiedz$Neither `start` nor `end` can be NaT)smsusrUz+'unit' must be one of 's', 'ms', 'us', 'ns'rUFround_okr)rdreroffsetrIcg|] }|j SrM)_valuers rO z1DatetimeArray._generate_range..s$;$;$;!QX$;$;$;rQr\rrcresorint64i8z datetime64[rZr)'rvalidate_periodsanyrcomcount_not_noner rrrr'_maybe_normalize_endpoints_infer_tz_from_endpoints_maybe_localize_pointrr6rHrr5r3_generate_ranger[arrayrris_utcr#r"tz_localize_to_utcr intlinspacerr\astypelenviewrPr)rrdrerrqrHrrrrrIleft_inclusiveright_inclusivei8valuesxdr endpoint_tzrstart_i8end_i8 dt64_valuesr\s rOrzDatetimeArray._generate_ranges^&w// )%r9kRRE'T2y+NNC   dD)) 0$)=!--d33E?sv'9//$//C$s ,, M1%gtRVWWW%S'$T8$;$;s$;$;$;28LLL&+&7%((SVK~+"5 '++ /t44E+> "+$/#    H$!--b)[IIE?//"iEEC 3((G AszEL8'QQQ, ~%%$??400 C<<! */ *#AbD> ''.Hs^^*F! - -%,#h--,HQK8> > ##E*** C<<=ty99 9==U=;;@ @rQTimestamp | NaTTypec.t||jS)NrH)rrHr&s rO_scalar_from_stringz!DatetimeArray._scalar_from_string's47++++rQrRcF|turdS||dSrL)r_assert_tzawareness_compat)rxothers rOr$z$DatetimeArray._check_compatible_with*s* C<< F ''.....rQrcr|d}tj||j|j}|S)Nr)rjrH)rr_from_value_and_resorurH)rxrr'tss rO _box_funczDatetimeArray._box_func2s1t   +E  P P P rQc|jS)a% 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. )rrs rOr\zDatetimeArray.dtype8s .{rQrVc.t|jddS)a* Return the timezone. Returns ------- zoneinfo.ZoneInfo,, datetime.tzinfo, pytz.tzinfo.BaseTZInfo, dateutil.tz.tz.tzfile, or None Returns None when the array is tz-naive. See Also -------- DatetimeIndex.tz_localize : Localize tz-naive DatetimeIndex to a given time zone, or remove timezone from a tz-aware DatetimeIndex. DatetimeIndex.tz_convert : Convert tz-aware DatetimeIndex from one time zone to another. 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[us, 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 rHN)getattrr\rs rOrHzDatetimeArray.tzQsNtz4...rQc td)NzNCannot directly set timezone. Use tz_localize() or tz_convert() as appropriate)AttributeErrorr&s rOrHzDatetimeArray.tzzs -   rQc|jS)z( Alias for tz attribute r+rs rOrzDatetimeArray.tzinfos wrQcDt|j|j|jS)zN Returns True if all of the dates are at midnight ("no time") ri)rasi8rHrurs rO is_normalizedzDatetimeArray.is_normalizeds ( 47MMMMrQrcDt|j|j|jS)Nri)rr;rHrurs rO_resolution_objzDatetimeArray._resolution_objsdit{CCCCrQ np.ndarraycj||jrt}t||Sr)rHobjectr __array__)rxr\rrs rOrBzDatetimeArray.__array__s0 =TW=Eww  u4 888rQr9c#tK|jdkr+tt|D] }||V dS|j}t|}t}||zdz}t|D]J}||z}t |dz|z|}t ||||jd|j}|Ed{VKdS)zt Return an iterator over the boxed values Yields ------ tstamp : Timestamp r  timestamp)rHboxrjN) ndimrangerr;_ITER_CHUNKSIZEminrrHru) rxirlength chunksizechunksstart_iend_i converteds rO__iter__zDatetimeArray.__iter__s 9q==3t99%%  1g   9DYYF'I )Q.F6]] % %i-QUi/88.'w#  %$$$$$$$$ % %rQTcXt|}||jkr|r|S|St|trt|t s#t ||S|jtdtj|j }t|j ||}t||||jS|jst#j|dr^t'|sOt)|r@t|j |d}t|||jS|j$t#j|drtd|j>t#j|dr)||jkrt'|rtdt|t*r||j St.j|||S) NrzCannot use .astype to convert from timezone-naive dtype to timezone-aware dtype. Use obj.tz_localize instead or series.dt.tz_localize insteadr\rqrTrzCannot 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.z]Casting to unit-less dtype 'datetime64' is not supported. Pass e.g. 'datetime64[ns]' instead.)rq)r-r\rrr/r.rrrH TypeErrorr[r^r_ndarraytyperrqrrrrr0rrDatetimeLikeArrayMixin)rxr\rnp_dtype res_valuesrs rOrzDatetimeArray.astypes+ U## DJ   #yy{{"K ~ . .4 3e_55 Www~~e$~777 48EI..0tTTT Dzz--jDI-VVV GOs++ && #5))  -T]EMMMJ::))*J>uz>22 2)00udCCCrQr)na_rep date_formatr[ str | floatnpt.NDArray[np.object_]c h| |jrd}tj|j|j|||jS)Nz%Y-%m-%d)rHformatr[rj)_is_dates_onlyrformat_array_from_datetimer;rHru)rxr[r\kwargss rO_format_native_typesz"DatetimeArray._format_native_typessB  4#6 $K/ I$'+f4;    rQct|dd}t|dd}t|tr |jj}|t urdS|j|t ddS|t ddS)Nrr\z;Cannot compare tz-naive and tz-aware datetime-like objects.z:Cannot compare tz-naive and tz-aware datetime-like objects)r6rr.r\rHrrU)rxr/other_tz other_dtypes rOr.z(DatetimeArray._assert_tzawareness_compats5(D11eWd33 k? 3 3 &{~H C<< D W_#Q$# L  rQrrct|trJ|j|d}n|} ||j}|jjdkr||j}t| ||j}|j r| }d|_ |j||j}n#t$r{tdr(tjdt"t%|d|z}t|||j}YnwxYw|S)NrJrperformance_warningszCNon-vectorized DateOffset being applied to Series or DatetimeIndex. stacklevelO)rr6rHr _apply_arrayrVr\rrrWrrrNotImplementedErrorr warningswarnr$r&rr)rxrryrZr|s rO _add_offsetzDatetimeArray._add_offset's}fd+++++ 7 %%d++FFF 5,,V_==J$++ (__V\:: $ZZ++Jj>N+OOF $))++# w"++DG44%# M M M011  %&/11  S))F2J$ZZ..z.LLFFF M( sAC))BE.-E.npt.NDArray[np.int64]c|jtj|jr|jSt |j|j|jS)z 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. Nri)rHrrr;r!rurs rOrnzDatetimeArray._local_timestampsPs? 7?i.tw77?9 "49dgDKHHHHrQctj|}|jtdt ||j}d}t |jtr|j}| |j ||S)aW 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') Nz?Cannot convert tz-naive timestamps, use tz_localize to localizerrT) rrrHrUrPrIrrqr6rrV)rxrHr\new_freqs rOrzDatetimeArray.tz_convert\sF #B ' ' 7?Q  BTY/// di & & !yH UJJJrQc|d}||vr$t|tstd|j3|"t |j|j|j}nFtdtj |}tj |j||||j}| d|j d}t||j }d}tj|s(t!|d krt#|d s|j}n||j|j}|||| S) a 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] )rr shift_forwardshift_backwardzoThe nonexistent argument must be one of 'raise', 'NaT', 'shift_forward', 'shift_backward' or a timedelta objectNriz,Already tz-aware, use tz_convert to convert.rrYrZrr rrT)rrrrHr!r;rurUrrr"rrrIrPrrr1rqr) rxrHrrnonexistent_options new_datesnew_dates_dt64r\rqs rOrzDatetimeArray.tz_localizes_dR 1 1 1* ; ; 1%  7 z/ 47UUU  NOOO'++B%7 #'k I#(:di(:(:(:;;BTY///  B   CIINN4q@Q;R;RN9DD ZDGO9De$GGGrQcDt|j|j|jS)ap Return an ndarray of ``datetime.datetime`` objects. Returns ------- numpy.ndarray An ndarray of ``datetime.datetime`` objects. See Also -------- DatetimeIndex.to_julian_date : Converts Datetime Array to float64 ndarray of Julian Dates. 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) )rHrjrr;rHrurs rO to_pydatetimezDatetimeArray.to_pydatetimejs,"$)dkJJJJrQcDt|j|j|j}||jj}t|||j}| d}|j| |j}|S)a 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[us, 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[us, Asia/Calcutta]', freq=None) ririnfer) rr;rHrurrVr\rWr _with_freqr)rx new_valuesr"dtas rOrzDatetimeArray.normalizesN-TYdkRRR  oodm&9:: 4jj$$[ 8I$JJnnW%% 7 //$'**C rQrEcddlm}|j(tjdt t |||jp|j}t|j tr.t|j drt|j j}|tdt!|}||}|}|j|j||jS) a 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]') rrDNzNConverting to PeriodArray/Index representation will drop timezone information.rj_period_dtype_codez8You must pass a freq argument as current index has none.r+)pandas.core.arraysrErHrorp UserWarningr&freqstrrrrqrhasattrr0_freqstrrr4_from_datetime64rV)rxrqrEress rOrzDatetimeArray.to_periodsf 322222 7  M2+--      <<54#5D$)Z00 7W /66 7#49--6| N#4((C{D+{+DM4DGLLLLrQc|}tj|d||j}||d}t r)ddlm}m}|||tj S|S) u 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. See Also -------- DatetimeIndex.day_name : Return the day names with specified locale. 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[us] >>> s.dt.month_name() 0 January 1 February 2 March dtype: str >>> idx = pd.date_range(start="2018-01", freq="ME", periods=3) >>> idx DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31'], dtype='datetime64[us]', freq='ME') >>> idx.month_name() Index(['January', 'February', 'March'], dtype='str') 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[us]', freq='ME') >>> idx.month_name(locale="pt_BR.utf8") # doctest: +SKIP Index(['Janeiro', 'Fevereiro', 'Março'], dtype='str') rlocalerjNrlr StringDtyperna_valuer rnrget_date_name_fieldrurwr pandasrrr[nanrxrryr|rpd_arrays rOrzDatetimeArray.month_namesj''))+ Ldk   ))&T)BB    H         8F++rv*F*F*FGGG G rQc|}tj|d||j}||d}t r)ddlm}m}|||tj S|S) u 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. See Also -------- DatetimeIndex.month_name : Return the month names with specified locale. 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[us] >>> s.dt.day_name() 0 Monday 1 Tuesday 2 Wednesday dtype: str >>> 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[us]', freq='D') >>> idx.day_name() Index(['Monday', 'Tuesday', 'Wednesday'], dtype='str') 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[us]', freq='D') >>> idx.day_name(locale="pt_BR.utf8") # doctest: +SKIP Index(['Segunda', 'Terça', 'Quarta'], dtype='str') rrNrrrrrrrs rOrzDatetimeArray.day_nameKsj''))+ JvDK   ))&T)BB    H         8F++rv*F*F*FGGG G rQcX|}t|d|jS)a[ Returns numpy array of :class:`datetime.time` objects. The time part of the Timestamps. See Also -------- DatetimeIndex.timetz : Returns numpy array of :class:`datetime.time` objects with timezones. The time part of the Timestamps. DatetimeIndex.date : 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[us, 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) rrErjrnrrurx timestampss rOrzDatetimeArray.times-R++-- !*&t{KKKKrQcFt|j|jd|jS)a Returns numpy array of :class:`datetime.time` objects with timezones. The time part of the Timestamps. See Also -------- DatetimeIndex.time : Returns numpy array of :class:`datetime.time` objects. The time part of the Timestamps. DatetimeIndex.tz : Return the timezone. 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[us, 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) rrr}rs rOrzDatetimeArray.timetzs"J"$)TW&t{SSSSrQcX|}t|d|jS)a Returns numpy array of python :class:`datetime.date` objects. Namely, the date part of Timestamps without time and timezone information. See Also -------- DatetimeIndex.time : Returns numpy array of :class:`datetime.time` objects. The time part of the Timestamps. DatetimeIndex.year : The year of the datetime. DatetimeIndex.month : The month as January=1, December=12. DatetimeIndex.day : The day of the datetime. 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[us, 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) rrrrs rOrzDatetimeArray.dates-T++-- !*&t{KKKKrQrBcddlm}|}tj||j}||gdd}|jrd|j|j<|S)a 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 r)rBri)rweekrUInt32)columnsr\N) rrBrnrbuild_isocalendar_sarrayru_hasnailoc_isnan)rxrBrysarrayiso_calendar_dfs rO isocalendarzDatetimeArray.isocalendars@ %$$$$$''))0dkJJJ#) 3338    ; 504O  -rQrYaN The year of the datetime. See Also -------- DatetimeIndex.month: The month as January=1, December=12. DatetimeIndex.day: The day 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[us] >>> datetime_series.dt.year 0 2000 1 2001 2 2002 dtype: int32 rhraE The month as January=1, December=12. See Also -------- DatetimeIndex.year: The year of the datetime. DatetimeIndex.day: The day of the datetime. 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[us] >>> datetime_series.dt.month 0 1 1 2 2 3 dtype: int32 rDa{ The day of the datetime. See Also -------- DatetimeIndex.year: The year of the datetime. DatetimeIndex.month: The month as January=1, December=12. DatetimeIndex.hour: The hours 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[us] >>> datetime_series.dt.day 0 1 1 2 2 3 dtype: int32 rha The hours of the datetime. See Also -------- DatetimeIndex.day: The day of the datetime. DatetimeIndex.minute: The minutes of the datetime. DatetimeIndex.second: The seconds 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[us] >>> datetime_series.dt.hour 0 0 1 1 2 2 dtype: int32 rmab The minutes of the datetime. See Also -------- DatetimeIndex.hour: The hours of the datetime. DatetimeIndex.second: The seconds 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[us] >>> datetime_series.dt.minute 0 0 1 1 2 2 dtype: int32 rra The seconds of the datetime. See Also -------- DatetimeIndex.minute: The minutes of the datetime. DatetimeIndex.microsecond: The microseconds of the datetime. DatetimeIndex.nanosecond: The nanoseconds 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[us] >>> datetime_series.dt.second 0 0 1 1 2 2 dtype: int32 rra The microseconds of the datetime. See Also -------- DatetimeIndex.second: The seconds of the datetime. DatetimeIndex.nanosecond: The nanoseconds 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[us] >>> datetime_series.dt.microsecond 0 0 1 1 2 2 dtype: int32 ra The nanoseconds of the datetime. See Also -------- DatetimeIndex.second: The seconds of the datetime. DatetimeIndex.microsecond: The microseconds 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 a 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 rdowrdoya( The ordinal day of the year. See Also -------- DatetimeIndex.dayofweek : The day of the week with Monday=0, Sunday=6. DatetimeIndex.day : The day of the datetime. 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[us, 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') rqaa The quarter of the date. See Also -------- DatetimeIndex.snap : Snap time stamps to nearest occurring frequency. DatetimeIndex.time : Returns numpy array of datetime.time objects. The time part of the Timestamps. 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[us, 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') rdima& The number of days in the month. See Also -------- Series.dt.day : Return the day of the month. Series.dt.is_month_end : Return a boolean indicating if the date is the last day of the month. Series.dt.is_month_start : Return a boolean indicating if the date is the first day of the month. Series.dt.month : Return the month as January=1 through December=12. 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[us, UTC] >>> s.dt.daysinmonth 0 31 1 29 dtype: int32 a 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[us] >>> 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]) rfirst) first_or_lastrlastra 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[us]', freq='D') >>> idx.is_quarter_start array([False, False, True, False]) ra 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[us]', freq='D') >>> idx.is_quarter_end array([False, True, False, False]) ra 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[us] >>> 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[us]', freq='D') >>> idx.is_year_start array([False, False, True]) This method, when applied to Series with datetime values under the ``.dt`` accessor, will lose information about Business offsets. >>> dates = pd.Series(pd.date_range("2020-10-30", periods=4, freq="BYS")) >>> dates 0 2021-01-01 1 2022-01-03 2 2023-01-02 3 2024-01-01 dtype: datetime64[us] >>> dates.dt.is_year_start 0 True 1 False 2 False 3 True dtype: bool >>> idx = pd.date_range("2020-10-30", periods=4, freq="BYS") >>> idx DatetimeIndex(['2021-01-01', '2022-01-03', '2023-01-02', '2024-01-01'], dtype='datetime64[us]', freq='BYS-JAN') >>> idx.is_year_start array([ True, True, True, True]) ra{ 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[us] >>> 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[us]', freq='D') >>> idx.is_year_end array([False, True, False]) ra 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. See Also -------- DatetimeIndex.is_year_end : Indicate whether the date is the last day of the year. DatetimeIndex.is_year_start : Indicate whether the date is the first day of a 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[us]', 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[us] >>> dates_series.dt.is_leap_year 0 True 1 False 2 False dtype: bool npt.NDArray[np.float64]c@tj|j}tj|j}tj|j}|dk}||xxdzcc<||xxdz cc<|tjd|zdz dz zd|zztj|dz ztj|d z z tj|d z zd z|j|jd z z|j d z z|j d z dz z|j d z dz zdz zS)a 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') rr rfiimdigC:A<ii@Biʚ;) r[asarrayrrhrtruncrrrrrr)rxrrhrtestarrs rOto_julian_datezDatetimeArray.to_julian_datesS2z$)$$ 4:&&j""!) W   g" he c)Q.// 0Dj htax   !htcz""  # htcz""  #    +"#+$%"T)E12/D(50 1   rQskipnakeepdimsr]r^rrc tj|f||d|}|rWt|tjr=|dkrddlm}||S|||jS|S)NrstdrTimedeltaArrayr) r_reducerr[rrrrr\)rxr]rrrcr|rrs rOrzDatetimeArray._reduce s!RfxRR6RR  E 62:66 Eu}}======%44V<<<**6*DDD rQr ddofrrCcddlm}|jjjdd}t j|}||j||} | |||||S)aC 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``. dtype : dtype, optional, default None Type to use in computing the standard deviation. For arrays of integer type the default is float64, for arrays of float types it is the same as the array type. out : ndarray, optional, default None Alternative output array in which to place the result. It must have the same shape as the expected output but the type (of the calculated values) will be cast if necessary. ddof : int, default 1 Degrees of Freedom. The divisor used in calculations is `N - ddof`, where `N` represents the number of elements. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array. If the default value is passed, then keepdims will not be passed through to the std method of sub-classes of ndarray, however any non-default value will be. If the sub-class method does not implement keepdims any exceptions will be raised. skipna : bool, default True Exclude NA/null values. If an entire row/column is ``NA``, the result will be ``NA``. Returns ------- Timedelta Standard deviation over requested axis. 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[us]', freq='D') >>> idx.std() Timedelta('1 days 00:00:00') rrr timedelta64r)axisoutrrr) rrrVr\r]replacer[rrr) rxrr\rrrrr dtype_strtdas rOrzDatetimeArray.std sF 655555M',44\=QQ ##((););E)B)B%(PPwwDcxPVwWWWrQ)rJr)rJr)ryrrqrr\rWrJr)rrrJr) rrrqrrrrrrr>rJr)NFrrr)rrrrrr>rr?rr=rIr@rJr)rJr))rJrR)rrrJr))rJrW)rJrV)rJr)rJr)NN)rJr?)rJr9)T)rr)r[r]rJr^)rrrJr)rJrr)rJr)rr)rr>rr?rJr)rJr^rL)rJrE)rJrB)rJr)r]r^rrrr)NNNr FT)rrrrrrrJrC)\r~ __module__ __qualname__r_typrr[r_recognized_scalarsr__annotations___infer_matchesrrrrorrrr__array_priority__r classmethodrr(rrrrrrr(r,r$r3r\rHsetterrr<r>rBrQrrdr.rqrnrr ravel_compatrr~rrrrrrrrrrrhrrrrrr_dayofweek_docrrrrrrrr _is_month_docr`rrrrrrrrrr __classcell__)rs@rOrrs 11f D#R]38(8(((((8N ///X/XIJ$766J6666Y+.D.D.DD ( ( (    $ 6555#E####  [ #';G [(.2NNNNN[N  >8;#*EEEEE[EN #*'.(.{D{D{D{D{D{D[{D@AAAA,,,,////  X*&/&/&/X&/PY  Y X NNNXN DDDXD 9999999%%%%:ADADADADADADADN(-$       0$$$$R I I I IPKPKPKPKd $+'. vHvHvHvHvHvKKKK0....`PMPMPMPMPMjBBBBBHCCCCCJ*L*L*LX*LX$T$T$TX$TL+L+L+LX+LZ))))V ?    D8 O    E8 /     C: ?    D:_  F8_  F:"/  K8!  J8!ND"/-GGKIG!/  !!KDIo  ""GF$O  M: K+MX%_*M,@,@w,@,W,WN#? (<(<6(<(R(RL'% ))T%_% ))NT$OA EEML"/' ++KX#?+ //Lb/ / / / j,0%         KXKXKXKXKXKXKXKXKXrQrFrrrr:rrrrrr>r tuple[np.ndarray, tzinfo | None]c t|||\}}t|dd}t}|tjd|d}|t kst |rttj|}d}tj |ddkr | tj }n|K|d krEtj |t } tj| |||t!| } | |fSt#|||d| \} } d}|r| r| } n| r| }| } nt%| ||| \} } | |fS|j}t'|t(r2tt*|}t-||j}|j} ntj|drMt'|t*r|j}ttj|}t%|||| \} }nb|jt4kr#| tj d}d}ttj|}||} |r| } t'| tjsJt;| | jjdksJ| jdksJt?| jsJ| |fS)aQ Parameters ---------- data : np.ndarray or ExtensionArray dtl.ensure_arraylike_for_datetimelike has already been called. copy : bool, default False tz : tzinfo or None, default None dayfirst : bool, default False yearfirst : bool, default False ambiguous : str, bool, or arraylike, default 'raise' See pandas._libs.tslibs.tzconversion.tz_localize_to_utc. out_unit : str or None, default None Desired output resolution. Returns ------- result : numpy.ndarray The sequence converted to a numpy array with dtype ``datetime64[unit]``. Where `unit` is "ns" unless specified otherwise by `out_unit`. tz : tzinfo or None Either the user-provided tzinfo or one inferred from the data. Raises ------ TypeError : PeriodDType data is passed r+r\NrYrZF)rintegerrr)rHrrr)rr allow_objectr)rHrrrrSM8) maybe_convert_dtyper6r(r[r\rAr,r rr infer_dtyperrrrarray_to_datetime_with_tzr#objects_to_datetime64_construct_from_dt64_naiverr.r_maybe_infer_tzrHrVrr)rrrWrr)rrrHrrrrr out_dtypeobj_datar|rP inferred_tz_s rOrrh sL%T4B777JD$w--JIH.8...// Vz::BJ%% ?4 . . .) ; ;;;rx((DD ^ W 4 4z$f555H4!#(22 F2: %:!#"! &&& "I{D k #  "7"49 2: Z *o..&M4(( R ) ) S ) )& dM * * !=DBJ%%1 Rdi    : $ $;;rxe;44DDBJ%%9%%  fbj ) )774<<77 ) <  # # # # <4     fl + +++ + 2:rQr?tuple[np.ndarray, bool]cX|j}t|s#t|}t||d}d}|jjdkr6||jd}|j}d}||j}|jdkr| }t|}tj | d|||}| |}||}|j|ksJ|j|}||fS) zP Convert datetime64 data to a supported dtype, localizing if necessary. Fr>|dvsJtj|tj}tj|||||t |\}}|||fS|jjdkr||fS|jtkr|r||fStdt|)a Convert data to array of timestamps. Parameters ---------- data : np.ndarray[object] dayfirst : bool yearfirst : bool utc : bool, default False Whether to convert/localize timestamps to UTC. errors : {'raise', 'coerce'} allow_object : bool Whether to return an object-dtype ndarray instead of raising if the data contains more than one timezone. out_unit : str or None, default None None indicates we should do resolution inference. Returns ------- result : ndarray np.datetime64[out_unit] if returned values represent wall times or UTC timestamps. object if mixed timezones inferred_tz : tzinfo or None If not None, then the datetime64 values in `result` denote UTC timestamps. Raises ------ ValueError : if data cannot be converted to datetimes TypeError : When a type cannot be converted to datetime )rcoercer)rrrrrNrz!DatetimeIndex has mixed timezones) r[robject_rarray_to_datetimer#r\rrArU) rrrrrrrr| tz_parseds rOrr sP ( ( ( ( ( :d"* - - -D/   ** FIy   c ! !y      %9$ $;<<<rQcDt|ds||fSt|jr0|td}d}nt j|jdst|jrtd|jdt|jtrtdt|jtr??#8%888  5$ % %  %00E#ZI$coeS99>~i&:2s&C&C~ A Is . ;;rdrrec" tj||}n"#t$r}td|d}~wwxYwtj|}tj|}|'|%tj||stdn||}|S)a 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 z>Start and end cannot both be tz-aware with different timezonesNz0Inferred time zone not equal to passed time zone)r infer_tzinfoAssertionErrorrUrr)rdrerHrerrs rOrr s,,UC88  L    (55K   # #B ~+1#K44 U !STT T U   Is 727r)tuple[_TimestampNoneT1, _TimestampNoneT2]cf|r,||}||}||fSrL)r)rdrers rOrrJ s="  OO%%E ?--//C #:rQr2Timestamp | Nonec|@|j9|dkr|nd}||dd}t|ts|||d<|jdi|}|S)a 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 NrF)rrrHrHrM)rrr6r)r2rqrHrr localize_argss rOrrW sq, ~")+"+g!5!5II5 &/ SWXX dD ! ! %T\"$M$  R^ , ,m , , IrQrrrrGenerator[Timestamp]c#Kt|}t|}|tur||}nd}t|}|tur||}nd}|rK||s6|jdkr||}n||}|||kr|jdkrd}d}| ||dz |zz}| ||dz |zz }tt|}tt|}|}|jdkra||krY|V||krdS| |}||}||krtd|d|}||kWdSdS||krY|V||krdS| |}||}||krtd|d|}||kWdSdS)a 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 Nrr zOffset z did not increment datez did not decrement date) r rrr is_on_offsetn rollforwardrollbackr _applyr)rdrerrrIcur next_dates rOrrx sW@v  F e  E C d## C..C #~~kk$ +V((//+ 8q==&&u--EEOOE**E3;;68q== {w{f,, }w{f,, E " "E y#  C C x1}}SjjIIIczz c**I!))$//IC !J6!J!J!JKKKCSjjjjjjSjjIIIczz c**I!))$//IC !J6!J!J!JKKKCSjjjjjjrQ).)rHrrIr@rJr.)rHrRrIr@rJrS)rU)rHrVrIr@rJrWrL)r]r^r_r^r`ra)rr:rrrHrVrrrrrr>rrarJr) rr?rHrVrrrr>rJr)FrFN) rr?rrrr;rrrrarJr)rrrHrV)rHrVrrVrJrV)F)rHrVrrrJrV)rdrrerrHrVrJrV)rdrFrerGrrrJr)r2rrJr) rdrrerrrrrrIr@rJr)r __future__rrrrtypingrrr r r ronumpyr[pandas._configr pandas._config.configr pandas._libsrrpandas._libs.tslibsrrrrrrrrrrrrrrrrr r!r"pandas._libs.tslibs.dtypesr# pandas.errorsr$pandas.util._decoratorsr%pandas.util._exceptionsr&pandas.util._validatorsr'pandas.core.dtypes.commonr(r)r*r+r,r-pandas.core.dtypes.dtypesr.r/r0pandas.core.dtypes.missingr1rr2rpandas.core.arrays._rangesr3pandas.core.commoncorecommonr pandas.tseries.frequenciesr4pandas.tseries.offsetsr5r6collections.abcr7r8r9pandas._typingr:r;r<r=r>r?r@rArrBrCrErFrGrHrPr TimelikeOps DatelikeOpsrrrrrrrrrrrrrMrQrOr6s>"""""" ------,,,,,,*:99999,,,,,,......444444666666 ,+++++222222====== 777777 D                    /.....w19dCCw19dCC IIII I OOOO O)-11111*"""""J Oq"Xq"Xq"Xq"Xq"XCOS_q"Xq"Xq"XvE&{{{{{{|))))`#*H H H H H V11111p:555r8=22222j((((V    BllllllrQ