o 5cI@sddlmZddlmZmZmZmZddlmZmZm Z ddl Z ddl Z ddl mZmZddlmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZm Z m!Z!m"Z"ddl#m$Z$ddl%m&Z&m'Z'dd l(m)Z)dd l*m+Z+dd l,m-Z-dd l.m/Z/m0Z0m1Z1m2Z2m3Z3m4Z4m5Z5m6Z6m7Z7m8Z8m9Z9m:Z:m;Z;mZ>dd l?m@Z@ddlAmBZBddlCmDZEddlFmGZGddlHmImJZKddlLmMZMddlNmOZOmPZPmQZQerddlRmSZSddlCmTZTmUZUeddZVdKdLddZWdMdNddZXGd d!d!eEjYeEjZZ[dOdPd&d'Z\  "  " " (dQd"d"d)dRd.d/Z] " ( " " "dSdTd3d4Z^dMdUd5d6Z_dVd8d9Z`d:d;ZadWd.f)__name____doc__property)rMrN docstringrlrJrkrK_field_accessorqs "rqcseZdZUdZdZeddZeejfZ e Z dZ e ddd Zgd Zd ed <d dgZd ed<gdZd ed<gdZd ed<eeeeZd ed<gdZd ed<dZded<dZded<eZeddZedefdfd!d" Zedd#d$dd'd(Zedd#dejd#d#d)fdd-d.Z e  # ) ) /dd0d1Z!ddd4d5Z"dd7d8Z#ddd9d:Z$ddd?Z&e ddAdBZ'e'j(dCdBZ'e ddDdEZ)e ddFdGZ*e ddIdJZ+ddɇfdKdL Z,dMdNZ-dddPdQZ.dddRddTdUZ/ddVdWZ0ddYdZZ1dd[d\Z2dd^d_Z3dd`daZ4e5j6dddbdcZ7ddddeZ8ddfdgZ9dddidjZ:ddldmZ;dddndoZe ddtduZ?e ddvdwZ@ddydzZAe d{d|ZBeBZCeDd}d~dZEeDdddZFeDdddZGeDdddZHeDdddZIeDdddZJeDdddZKeDdddZLdZMeDddeMZNeNZOeNZPeDdddZQeQZReDdddZSeDdddZTeTZUdZVeDddeVjWddZXeDddeVjWddZYeDdddZZeDdddZ[eDdddZ\eDdddZ]eDdddZ^dddZ_    # OddddZ`ZaS) 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 ---------- values : Series, Index, DatetimeArray, ndarray The datetime data. For DatetimeArray `values` (or a Series or Index boxing one), `dtype` and `freq` will be extracted from `values`. dtype : numpy.dtype or DatetimeTZDtype Note that the only NumPy dtype allowed is 'datetime64[ns]'. freq : str or Offset, optional The frequency. copy : bool, default False Whether to copy the underlying array of values. Attributes ---------- None Methods ------- None datetimearrayr r@)r datetime64datereturntype[Timestamp]cCstSN)rrgrJrJrK _scalar_typeszDatetimeArray._scalar_type)is_month_start is_month_endis_quarter_startis_quarter_end is_year_start is_year_end is_leap_yearz list[str]r[r]rArd)yearrSdayhourminutesecond weekofyearweekweekday dayofweek day_of_week dayofyear day_of_yearquarter days_in_month daysinmonth microsecond nanosecond _field_ops)rurtimetz _other_ops_datetimelike_ops) to_period tz_localize tz_convert normalizestrftimeroundfloorceil month_nameday_name_datetimelike_methodsinp.dtype | DatetimeTZDtype_dtypeNBaseOffset | None_freqcCst|jt|}|Srx)_validate_dt64_dtyperI)clsrhrIrJrJrK_validate_dtypes zDatetimeArray._validate_dtyperh np.ndarraycsrt|tjsJ|jdksJt|tjr#||jksJt|r"Jn |jt|jks-Jt ||}||_ |S)NM) isinstancerHndarraykindrIrrbrsuper _simple_newr)rrhr]rIrj __class__rJrKr s zDatetimeArray._simple_newFrIcopyrboolcCs|j|||dS)Nr)_from_sequence_not_strict)rscalarsrIrrJrJrK_from_sequenceszDatetimeArray._from_sequenceraise'str | BaseOffset | lib.NoDefault | Nonedayfirst yearfirstc  Cs|du} |tjur |nd}t|\}} t|||||||d\} }} t|| | \}} | r/d}t|}|j| ||d} | durM|durM|j| ||d| S| rUt | j | _ | S)N)rIrrArr ambiguousr]rIr) r no_defaultdtlmaybe_infer_freq_sequence_to_dt64nsvalidate_inferred_freqrLr_validate_frequencyr inferred_freqr)rdatarIrrAr]rrr explicit_none freq_infersubarrrrjrJrJrKr"s.    z'DatetimeArray._from_sequence_not_strictbothc Cst|}|durtdd|||fDrtdt||||dkr'tdt|}|dur3t|}|dur;t|}|tusC|turGtdt | \} } t |||\}}} t |||}|dur|durfdn|j } |durodn|j }t || |||||}t |||||||}|durt|tr|dur|d}|dur|d}t|trt||||}nt||||d}tjdd |Dtjd }|dur|j n|j }|dur|durt|stj||||d }|dur||||}|dur||||}ntjd |j|j|d d |j}|jdkr|d}||kr'| s&| s&|dd}n:t|j}t|j}| r7| sa| sLt|rL|d |krL|dd}| sat|ra|d|kra|dd}| d}t!|}|j"|||dS)Ncss|]}|duVqdSrxrJ.0xrJrJrK ^sz0DatetimeArray._generate_range..z1Must provide freq argument if no data is suppliedzVOf the four parameters: start, end, periods, and freq, exactly three must be specifiedz$Neither `start` nor `end` can be NaT)rOrPperiodsoffsetcSsg|]}|jqSrJ)valuerrJrJrK sz1DatetimeArray._generate_range..rI)r nonexistentrint64i8zdatetime64[ns]r)#rvalidate_periodsany ValueErrorcomcount_not_nonerrr r"_maybe_normalize_endpoints_infer_tz_from_endpointsrA_maybe_localize_pointrr:rr;r7generate_rangerHarrayrris_utcrtz_localize_to_utclinspacerrIastypelenviewrLr)rrOrPrr]rArrr inclusiveleft_inclusiveright_inclusive _normalizedstart_tzend_tzi8valuesxdr endpoint_tzstart_i8end_i8 dt64_valuesrIrJrJrK_generate_rangeOs                    zDatetimeArray._generate_rangesetitem np.datetime64cCs0t||js|turtd|j||d|jS)Nz'value' should be a Timestamp.)r)rrzr r_check_compatible_withasm8)rgrrrJrJrK _unbox_scalarszDatetimeArray._unbox_scalarTimestamp | NaTTypecCst||jdS)NrA)rrArgrrJrJrK_scalar_from_stringsz!DatetimeArray._scalar_from_stringcCsl|turdS|||r0|jdur2t|j|js4tjdttdt d|jd|jddSdSdS)NaSetitem-like behavior with mismatched timezones is deprecated and will change in a future version. Instead of raising (or for Index, Series, and DataFrame methods, coercing to object dtype), the value being set (or passed as a fill_value, or inserted) will be cast to the existing DatetimeArray/DatetimeIndex/Series/DataFrame column's timezone. To retain the old behavior, explicitly cast to object dtype before the operation. stacklevelzTimezones don't match. 'z' != '') r _assert_tzawareness_compatrAr tz_comparewarningswarn FutureWarningr!r)rgotherrrJrJrKrs  z$DatetimeArray._check_compatible_withrcCs6|d}tj||j|jd}|tur||j|S)Nr)rUrA)rr_from_value_and_resorbrAr _set_freqr])rgrrtsrJrJrK _box_funcs  zDatetimeArray._box_funccC|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. )rryrJrJrKrIszDatetimeArray.dtyperBcCst|jddS)z Return the timezone. Returns ------- datetime.tzinfo, pytz.tzinfo.BaseTZInfo, dateutil.tz.tz.tzfile, or None Returns None when the array is tz-naive. rAN)getattrrIryrJrJrKrAs zDatetimeArray.tzcCstd)NzNCannot directly set timezone. Use tz_localize() or tz_convert() as appropriate)AttributeErrorrrJrJrKrAscCr )z( Alias for tz attribute rryrJrJrKrszDatetimeArray.tzinfocCt|j|j|jdS)zN Returns True if all of the dates are at midnight ("no time") rT)rasi8rArbryrJrJrK is_normalized$szDatetimeArray.is_normalizedrcCr)NrT)rrrArbryrJrJrK_resolution_obj+szDatetimeArray._resolution_objcs |dur |jr t}tj|dS)Nr)rAobjectr __array__)rgrIrrJrKr2szDatetimeArray.__array__c cs|jdkrtt|D]}||Vq dS|j}t|}d}||d}t|D]$}||}t|d||}t||||j|jd|jd}|EdHq)dS)zt Return an iterator over the boxed values Yields ------ tstamp : Timestamp ri' timestamp)rAr]boxrUN) ndimrangerrminrrAr]rb) rgirlength chunksizechunksstart_iend_i convertedrJrJrK__iter__9s*      zDatetimeArray.__iter__TcCs0t|}t||jr|r|S|S|jdur7t|r7t|s7tt|r7t |j |dd}t |j ||jdSt |rCt|||ddS|jdurdt|trdt|j}t |j ||d}t |j ||dS|jdurt|r||jkrt|rtjdttd|j |St|r|j|jdStj|||S) NTrrF)via_utcz}Passing unit-less datetime64 dtype to .astype is deprecated and will raise in a future version. Pass 'datetime64[ns]' insteadr)r])r3r+rIrrAr(rrrr_ndarraytyperr)r#rr4rHrDrrrr!rr/rr]rDatetimeLikeArrayMixin)rgrIr res_valuesnp_dtyperJrJrKrWsL    zDatetimeArray.astype)na_rep date_formatnpt.NDArray[np.object_]cKs0ddlm}|||}tj|j|j|||jdS)Nr)!get_format_datetime64_from_values)rAformatr*rU)pandas.io.formats.formatr-r format_array_from_datetimerrArb)rgr*r+kwargsr-fmtrJrJrK_format_native_typess  z"DatetimeArray._format_native_typescCs6t|tjr t|}t|dsdS|j}t|j|S)NrF)rrHrtrhasattrrrr)rgrother_tzrJrJrK _has_same_tzs  zDatetimeArray._has_same_tzNonecCsft|dd}t|dd}t|r|jj}|turdS|jdur)|dur'tddS|dur1tddS)NrrIz;Cannot compare tz-naive and tz-aware datetime-like objects.z:Cannot compare tz-naive and tz-aware datetime-like objects)rr*rIrAr TypeError)rgrr5 other_dtyperJrJrKrs"   z(DatetimeArray._assert_tzawareness_compatcCst|trJ|jdur|d}n|}z |||j}Wn-tyLtj dt t d| d|}t ||}t|sI||jYSY|Swtj||jd}|jdur`||j}|S)NzCNon-vectorized DateOffset being applied to Series or DatetimeIndex.rOr)rr;rAr _apply_arrayrrINotImplementedErrorrrr r!rr&rrrrr)rgrrhrjrJrJrK _add_offsets.      zDatetimeArray._add_offsetnpt.NDArray[np.int64]cCs0|jdus t|jr|jSt|j|j|jdS)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. NrT)rArrrrrbryrJrJrKrZszDatetimeArray._local_timestampscCs>t|}|jdurtdt||jd}|j|j||jdS)a Convert tz-aware Datetime Array/Index from one time zone to another. Parameters ---------- tz : str, pytz.timezone, dateutil.tz.tzfile or None Time zone for time. Corresponding timestamps would be converted to this time zone of the Datetime Array/Index. A `tz` of None will convert to UTC and remove the timezone information. Returns ------- Array or Index Raises ------ TypeError If Datetime Array/Index is tz-naive. See Also -------- DatetimeIndex.tz : A timezone that has a variable offset from UTC. DatetimeIndex.tz_localize : Localize tz-naive DatetimeIndex to a given time zone, or remove timezone from a tz-aware DatetimeIndex. Examples -------- With the `tz` parameter, we can change the DatetimeIndex to other time zones: >>> dti = pd.date_range(start='2014-08-01 09:00', ... freq='H', periods=3, tz='Europe/Berlin') >>> dti DatetimeIndex(['2014-08-01 09:00:00+02:00', '2014-08-01 10:00:00+02:00', '2014-08-01 11:00:00+02:00'], dtype='datetime64[ns, Europe/Berlin]', freq='H') >>> dti.tz_convert('US/Central') DatetimeIndex(['2014-08-01 02:00:00-05:00', '2014-08-01 03:00:00-05:00', '2014-08-01 04:00:00-05:00'], dtype='datetime64[ns, US/Central]', freq='H') With the ``tz=None``, we can remove the timezone (after converting to UTC if necessary): >>> dti = pd.date_range(start='2014-08-01 09:00', freq='H', ... periods=3, tz='Europe/Berlin') >>> dti DatetimeIndex(['2014-08-01 09:00:00+02:00', '2014-08-01 10:00:00+02:00', '2014-08-01 11:00:00+02:00'], dtype='datetime64[ns, Europe/Berlin]', freq='H') >>> dti.tz_convert(None) DatetimeIndex(['2014-08-01 07:00:00', '2014-08-01 08:00:00', '2014-08-01 09:00:00'], dtype='datetime64[ns]', freq='H') Nz?Cannot convert tz-naive timestamps, use tz_localize to localizerCrIr]) r maybe_get_tzrAr8rL_unitrr%r])rgrArIrJrJrKrs @ zDatetimeArray.tz_convertcCsd}||vrt|tstd|jdur$|dur t|j|j}ntdt|}t j |j||||j d}| d|j d}t||j d}d}t|sYt|d kr]t|d s]|j}n |duri|jduri|j}|j|||d 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, pytz.timezone, dateutil.tz.tzfile or None Time zone to convert timestamps to. Passing ``None`` will remove the time zone information preserving local time. ambiguous : 'infer', 'NaT', bool array, default 'raise' When clocks moved backward due to DST, ambiguous times may arise. For example in Central European Time (UTC+01), when going from 03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at 00:30:00 UTC and at 01:30:00 UTC. In such a situation, the `ambiguous` parameter dictates how ambiguous times should be handled. - 'infer' will attempt to infer fall dst-transition hours based on order - bool-ndarray where True signifies a DST time, False signifies a non-DST time (note that this flag is only applicable for ambiguous times) - 'NaT' will return NaT where there are ambiguous times - 'raise' will raise an AmbiguousTimeError if there are ambiguous times. nonexistent : 'shift_forward', 'shift_backward, 'NaT', timedelta, default 'raise' A nonexistent time does not exist in a particular timezone where clocks moved forward due to DST. - 'shift_forward' will shift the nonexistent time forward to the closest existing time - 'shift_backward' will shift the nonexistent time backward to the closest existing time - 'NaT' will return NaT where there are nonexistent times - timedelta objects will shift nonexistent times by the timedelta - 'raise' will raise an NonExistentTimeError if there are nonexistent times. Returns ------- Same type as self Array/Index converted to the specified time zone. Raises ------ TypeError If the Datetime Array/Index is tz-aware and tz is not None. See Also -------- DatetimeIndex.tz_convert : Convert tz-aware DatetimeIndex from one time zone to another. Examples -------- >>> tz_naive = pd.date_range('2018-03-01 09:00', periods=3) >>> tz_naive DatetimeIndex(['2018-03-01 09:00:00', '2018-03-02 09:00:00', '2018-03-03 09:00:00'], dtype='datetime64[ns]', freq='D') Localize DatetimeIndex in US/Eastern time zone: >>> tz_aware = tz_naive.tz_localize(tz='US/Eastern') >>> tz_aware DatetimeIndex(['2018-03-01 09:00:00-05:00', '2018-03-02 09:00:00-05:00', '2018-03-03 09:00:00-05:00'], dtype='datetime64[ns, US/Eastern]', freq=None) With the ``tz=None``, we can remove the time zone information while keeping the local time (not converted to UTC): >>> tz_aware.tz_localize(None) DatetimeIndex(['2018-03-01 09:00:00', '2018-03-02 09:00:00', '2018-03-03 09:00:00'], dtype='datetime64[ns]', freq=None) Be careful with DST changes. When there is sequential data, pandas can infer the DST time: >>> s = pd.to_datetime(pd.Series(['2018-10-28 01:30:00', ... '2018-10-28 02:00:00', ... '2018-10-28 02:30:00', ... '2018-10-28 02:00:00', ... '2018-10-28 02:30:00', ... '2018-10-28 03:00:00', ... '2018-10-28 03:30:00'])) >>> s.dt.tz_localize('CET', ambiguous='infer') 0 2018-10-28 01:30:00+02:00 1 2018-10-28 02:00:00+02:00 2 2018-10-28 02:30:00+02:00 3 2018-10-28 02:00:00+01:00 4 2018-10-28 02:30:00+01:00 5 2018-10-28 03:00:00+01:00 6 2018-10-28 03:30:00+01:00 dtype: datetime64[ns, CET] In some cases, inferring the DST is impossible. In such cases, you can pass an ndarray to the ambiguous parameter to set the DST explicitly >>> s = pd.to_datetime(pd.Series(['2018-10-28 01:20:00', ... '2018-10-28 02:36:00', ... '2018-10-28 03:46:00'])) >>> s.dt.tz_localize('CET', ambiguous=np.array([True, True, False])) 0 2018-10-28 01:20:00+02:00 1 2018-10-28 02:36:00+02:00 2 2018-10-28 03:46:00+01:00 dtype: datetime64[ns, CET] If the DST transition causes nonexistent times, you can shift these dates forward or backwards with a timedelta object or `'shift_forward'` or `'shift_backwards'`. >>> s = pd.to_datetime(pd.Series(['2015-03-29 02:30:00', ... '2015-03-29 03:30:00'])) >>> s.dt.tz_localize('Europe/Warsaw', nonexistent='shift_forward') 0 2015-03-29 03:00:00+02:00 1 2015-03-29 03:30:00+02:00 dtype: datetime64[ns, Europe/Warsaw] >>> s.dt.tz_localize('Europe/Warsaw', nonexistent='shift_backward') 0 2015-03-29 01:59:59.999999999+01:00 1 2015-03-29 03:30:00+02:00 dtype: datetime64[ns, Europe/Warsaw] >>> s.dt.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta('1H')) 0 2015-03-29 03:30:00+02:00 1 2015-03-29 03:30:00+02:00 dtype: datetime64[ns, Europe/Warsaw] )rr shift_forwardshift_backwardzoThe nonexistent argument must be one of 'raise', 'NaT', 'shift_forward', 'shift_backward' or a timedelta objectNz,Already tz-aware, use tz_convert to convert.)rrrUrErFr?rrr@)rrrrArrr8rrArrrbrrBrLrrr5r]r)rgrArrnonexistent_options new_datesrIr]rJrJrKr;s8   "zDatetimeArray.tz_localizecCr)z Return an ndarray of datetime.datetime objects. Returns ------- datetimes : ndarray[object] )rArUrrrArbryrJrJrK to_pydatetimeszDatetimeArray.to_pydatetimecCsZt|j|j|jd}||jj}t|j||jd}| d}|jdur+| |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[ns, Asia/Calcutta]', freq='H') >>> idx.normalize() DatetimeIndex(['2014-08-01 00:00:00+05:30', '2014-08-01 00:00:00+05:30', '2014-08-01 00:00:00+05:30'], dtype='datetime64[ns, Asia/Calcutta]', freq=None) rTrinferN) rrrArbrr%rIr&r _with_freqr)rg new_valuesrdtarJrJrKrs&   zDatetimeArray.normalizer>cCsxddlm}|jdurtjdttd|dur2|jp|j}|dur&t dt |}|dur0|}|}|j |j ||jdS)ax Cast to PeriodArray/Index at a particular frequency. Converts DatetimeArray/Index to PeriodArray/Index. Parameters ---------- freq : str or Offset, optional One of pandas' :ref:`offset strings ` or an Offset object. Will be inferred by default. Returns ------- PeriodArray/Index 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]') r)r>NzNConverting to PeriodArray/Index representation will drop timezone information.rz8You must pass a freq argument as current index has none.r) pandas.core.arraysr>rArr UserWarningr!rarrr8_from_datetime64r%)rgr]r>resrJrJrKr+s$ ,  zDatetimeArray.to_periodr?cCsZtjdttdddlm}|jjdkrtd|j | | j }| d}||S)a  Calculate deltas between self values and self converted to Periods at a freq. Used for vectorized offsets. Parameters ---------- freq : Period frequency Returns ------- TimedeltaArray/Index zto_perioddelta is deprecated and will be removed in a future version. Use `dtindex - dtindex.to_period(freq).to_timestamp()` instead.rrr?M8[ns]z)Only supported for nanosecond resolution.zm8[ns]) rrrr!pandas.core.arrays.timedeltasr?r%rIr<rr to_timestampr)rgr]r?i8deltam8deltarJrJrKto_perioddeltass   zDatetimeArray.to_perioddeltacC.|}tj|d||jd}|j|dd}|S)a 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. Returns ------- Series or Index Series or Index of month names. Examples -------- >>> s = pd.Series(pd.date_range(start='2018-01', freq='M', periods=3)) >>> s 0 2018-01-31 1 2018-02-28 2 2018-03-31 dtype: datetime64[ns] >>> s.dt.month_name() 0 January 1 February 2 March dtype: object >>> idx = pd.date_range(start='2018-01', freq='M', periods=3) >>> idx DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31'], dtype='datetime64[ns]', freq='M') >>> idx.month_name() Index(['January', 'February', 'March'], dtype='object') rlocalerUNrVrZrrerbrfrgrZrhrjrJrJrKr $ zDatetimeArray.month_namecCrX)a 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. Returns ------- Series or Index Series or Index of day names. Examples -------- >>> s = pd.Series(pd.date_range(start='2018-01-01', freq='D', periods=3)) >>> s 0 2018-01-01 1 2018-01-02 2 2018-01-03 dtype: datetime64[ns] >>> s.dt.day_name() 0 Monday 1 Tuesday 2 Wednesday dtype: object >>> idx = pd.date_range(start='2018-01-01', freq='D', periods=3) >>> idx DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'], dtype='datetime64[ns]', freq='D') >>> idx.day_name() Index(['Monday', 'Tuesday', 'Wednesday'], dtype='object') rrYNrVr[r\rJrJrKrr]zDatetimeArray.day_namecC|}t|d|jdS)zr Returns numpy array of :class:`datetime.time` objects. The time part of the Timestamps. rrrUrZrrbrg timestampsrJrJrKrs zDatetimeArray.timecCst|j|jd|jdS)z Returns numpy array of :class:`datetime.time` objects with timezones. The time part of the Timestamps. rr_rGryrJrJrKrszDatetimeArray.timetzcCr^)z Returns numpy array of python :class:`datetime.date` objects. Namely, the date part of Timestamps without time and timezone information. rur_r`rarJrJrKrus zDatetimeArray.dater=cCsLddlm}|}tj||jd}||gddd}|jr$d|j|j<|S)a Calculate year, week, and day according to the ISO 8601 standard. .. versionadded:: 1.1.0 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 rr<rT)rrrUInt32)columnsrIN) pandasr=rZrbuild_isocalendar_sarrayrb_hasnailoc_isnan)rgr=rhsarrayiso_calendar_dfrJrJrK isocalendars "  zDatetimeArray.isocalendarcCs>tjdttd|j}|jr|jdtj dS|jddS)z The week ordinal of the year. .. deprecated:: 1.1.0 weekofyear and week have been deprecated. Please use DatetimeIndex.isocalendar().week instead. zweekofyear and week have been deprecated, please use DatetimeIndex.isocalendar().week instead, which returns a Series. To exactly reproduce the behavior of week and weekofyear and return an Index, you may call pd.Int64Index(idx.isocalendar().week)rrX)rIna_valuerr) rrrr!rlrhasnansto_numpyrHnan)rg week_seriesrJrJrKrAs   zDatetimeArray.weekofyearrYa The year of the datetime. Examples -------- >>> datetime_series = pd.Series( ... pd.date_range("2000-01-01", periods=3, freq="Y") ... ) >>> datetime_series 0 2000-12-31 1 2001-12-31 2 2002-12-31 dtype: datetime64[ns] >>> datetime_series.dt.year 0 2000 1 2001 2 2002 dtype: int64 rSra The month as January=1, December=12. Examples -------- >>> datetime_series = pd.Series( ... pd.date_range("2000-01-01", periods=3, freq="M") ... ) >>> datetime_series 0 2000-01-31 1 2000-02-29 2 2000-03-31 dtype: datetime64[ns] >>> datetime_series.dt.month 0 1 1 2 2 3 dtype: int64 rDa The day of the datetime. Examples -------- >>> datetime_series = pd.Series( ... pd.date_range("2000-01-01", periods=3, freq="D") ... ) >>> datetime_series 0 2000-01-01 1 2000-01-02 2 2000-01-03 dtype: datetime64[ns] >>> datetime_series.dt.day 0 1 1 2 2 3 dtype: int64 rha The hours of the datetime. Examples -------- >>> datetime_series = pd.Series( ... pd.date_range("2000-01-01", periods=3, freq="h") ... ) >>> datetime_series 0 2000-01-01 00:00:00 1 2000-01-01 01:00:00 2 2000-01-01 02:00:00 dtype: datetime64[ns] >>> datetime_series.dt.hour 0 0 1 1 2 2 dtype: int64 rma The minutes of the datetime. Examples -------- >>> datetime_series = pd.Series( ... pd.date_range("2000-01-01", periods=3, freq="T") ... ) >>> datetime_series 0 2000-01-01 00:00:00 1 2000-01-01 00:01:00 2 2000-01-01 00:02:00 dtype: datetime64[ns] >>> datetime_series.dt.minute 0 0 1 1 2 2 dtype: int64 rsa The seconds of the datetime. Examples -------- >>> datetime_series = pd.Series( ... pd.date_range("2000-01-01", periods=3, freq="s") ... ) >>> datetime_series 0 2000-01-01 00:00:00 1 2000-01-01 00:00:01 2 2000-01-01 00:00:02 dtype: datetime64[ns] >>> datetime_series.dt.second 0 0 1 1 2 2 dtype: int64 rusa The microseconds of the datetime. Examples -------- >>> datetime_series = pd.Series( ... pd.date_range("2000-01-01", periods=3, freq="us") ... ) >>> datetime_series 0 2000-01-01 00:00:00.000000 1 2000-01-01 00:00:00.000001 2 2000-01-01 00:00:00.000002 dtype: datetime64[ns] >>> datetime_series.dt.microsecond 0 0 1 1 2 2 dtype: int64 ra The nanoseconds of the datetime. Examples -------- >>> datetime_series = pd.Series( ... pd.date_range("2000-01-01", periods=3, freq="ns") ... ) >>> datetime_series 0 2000-01-01 00:00:00.000000000 1 2000-01-01 00:00:00.000000001 2 2000-01-01 00:00:00.000000002 dtype: datetime64[ns] >>> datetime_series.dt.nanosecond 0 0 1 1 2 2 dtype: int64 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: int64 rdowrdoyz. The ordinal day of the year. rqz* The quarter of the date. rdimz2 The number of days in the month. 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[ns] >>> s.dt.is_month_start 0 False 1 False 2 True dtype: bool >>> s.dt.is_month_end 0 False 1 True 2 False dtype: bool >>> idx = pd.date_range("2018-02-27", periods=3) >>> idx.is_month_start array([False, False, True]) >>> idx.is_month_end array([False, True, False]) r{first) first_or_lastr|lastr}a 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 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_start=df.dates.dt.is_quarter_start) dates quarter is_quarter_start 0 2017-03-30 1 False 1 2017-03-31 1 False 2 2017-04-01 2 True 3 2017-04-02 2 False >>> idx = pd.date_range('2017-03-30', periods=4) >>> idx DatetimeIndex(['2017-03-30', '2017-03-31', '2017-04-01', '2017-04-02'], dtype='datetime64[ns]', freq='D') >>> idx.is_quarter_start array([False, False, True, False]) r~a Indicator for whether the date is the last day of a quarter. Returns ------- is_quarter_end : Series or DatetimeIndex The same type as the original data with boolean values. Series will have the same name and index. DatetimeIndex will have the same name. See Also -------- quarter : Return the quarter of the date. is_quarter_start : Similar property indicating the quarter start. Examples -------- This method is available on Series with datetime values under the ``.dt`` accessor, and directly on DatetimeIndex. >>> df = pd.DataFrame({'dates': pd.date_range("2017-03-30", ... periods=4)}) >>> df.assign(quarter=df.dates.dt.quarter, ... is_quarter_end=df.dates.dt.is_quarter_end) dates quarter is_quarter_end 0 2017-03-30 1 False 1 2017-03-31 1 True 2 2017-04-01 2 False 3 2017-04-02 2 False >>> idx = pd.date_range('2017-03-30', periods=4) >>> idx DatetimeIndex(['2017-03-30', '2017-03-31', '2017-04-01', '2017-04-02'], dtype='datetime64[ns]', freq='D') >>> idx.is_quarter_end array([False, True, False, False]) 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[ns] >>> dates.dt.is_year_start 0 False 1 False 2 True dtype: bool >>> idx = pd.date_range("2017-12-30", periods=3) >>> idx DatetimeIndex(['2017-12-30', '2017-12-31', '2018-01-01'], dtype='datetime64[ns]', freq='D') >>> idx.is_year_start array([False, False, True]) 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[ns] >>> dates.dt.is_year_end 0 False 1 True 2 False dtype: bool >>> idx = pd.date_range("2017-12-30", periods=3) >>> idx DatetimeIndex(['2017-12-30', '2017-12-31', '2018-01-01'], dtype='datetime64[ns]', freq='D') >>> idx.is_year_end array([False, True, False]) 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. 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="Y") >>> idx DatetimeIndex(['2012-12-31', '2013-12-31', '2014-12-31'], dtype='datetime64[ns]', freq='A-DEC') >>> idx.is_leap_year array([ True, False, False]) >>> dates_series = pd.Series(idx) >>> dates_series 0 2012-12-31 1 2013-12-31 2 2014-12-31 dtype: datetime64[ns] >>> dates_series.dt.is_leap_year 0 True 1 False 2 False dtype: bool npt.NDArray[np.float64]cCst|j}t|j}t|j}|dk}||d8<||d7<|td|ddd|t|dt|d t|d d |j|jd |j d |j d d|j d ddS)z Convert Datetime Array to float64 ndarray of Julian Dates. 0 Julian date is noon January 1, 4713 BC. https://en.wikipedia.org/wiki/Julian_day rrrQiimdigC:A<ii@Biʚ;) rHasarrayrrSrfixrrrrrr)rgrrSrtestarrrJrJrKto_julian_dateZs@        zDatetimeArray.to_julian_daterddofintkeepdimsskipnac CsRddlm}|jjjdd}t|}|j|j||d} | j |||||dS)a Return sample standard deviation over requested axis. Normalized by N-1 by default. This can be changed using the ddof argument Parameters ---------- axis : int optional, default None Axis for the function to be applied on. For `Series` this parameter is unused and defaults to `None`. ddof : int, default 1 Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. skipna : bool, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA. Returns ------- Timedelta rrQrt timedelta64r)axisoutrrr) rMr?r%rIrMreplacerHrrstd) rgrrIrrrrr? dtype_strtdarJrJrKr}s ! zDatetimeArray.std)rvrw)rhrr]rrvrr)rr)rrr]rrrrr)NFrrrF)rrrvr)rvr)rr)rrrvr)rvr)rvrB)rvr)rvrrx)rvr)T)rvr,)rvr7)rvrr)rvr>)rr)rvr>)rvr?)rvr=)rvr)NNNrFT)rrrrrr)brm __module__ __qualname__rn_typrHrt_internal_fill_valuer_recognized_scalarsr'_is_recognized_dtype_infer_matchesrorzr[__annotations__rdrrrr__array_priority__rr$_default_dtype classmethodrrrr rrrrrrr rIrAsetterrrrrr"rr3r6rr=rZrr ravel_compatrrHrrrWrrrrrurlrrrqrrSrrrrrr_dayofweek_docrrrrrrrr _is_month_docr.r{r|r}r~rrrrr __classcell__rJrJrrKrrsz "      , q          9   " L 7 / H # ,,    -  ",**,, *%rrFrequire_iso8601rrvcCs0t|d|d\}}}t|}tj|||d}|S)zV Parse/convert the passed data to either DatetimeArray or np.ndarray[object]. T allow_mixedrr)rrLrrr)rrrjrAr]rIrLrJrJrKsequence_to_datetimess rrrrrrrcCs0d} t|}t|}t||}tj||dd\}}t|tr"|j} t |||d\}}t |dd} t | s=t | s=t | rd}tj|dddkrO|tj}n:t|||d||d \}} |r| r|jd krl|t|dfSt|rw|d } nt|d |} | t}n| r| }|j} t| rt||j}|j} nVt| rt |d |}|jtkrt|td }d}|durt|}tj |d ||d }|t}|jtksJ|j|} n|rt|}|jt!kr|jtjdd}|t} |r| "} t| tj#sJt$| | jdksJ| jt||| || fS)a Parameters ---------- data : list-like dtype : dtype, str, or None, default None copy : bool, default False tz : tzinfo, str, or None, default None dayfirst : bool, default False yearfirst : bool, default False ambiguous : str, bool, or arraylike, default 'raise' See pandas._libs.tslibs.tzconversion.tz_localize_to_utc. allow_mixed : bool, default False Interpret integers as timestamps when datetime objects are also present. require_iso8601 : bool, default False Only consider ISO-8601 formats when parsing strings. Returns ------- result : numpy.ndarray The sequence converted to a numpy array with dtype ``datetime64[ns]``. tz : tzinfo or None Either the user-provided tzinfo or one inferred from the data. inferred_freq : Tick or None The inferred frequency of the sequence. Raises ------ TypeError : PeriodDType data is passed Nrr)cls_namerrIF)rinteger)rr allow_objectrrrr%rrr#rR)%rrrAvalidate_tz_from_dtyper!ensure_arraylike_for_datetimelikerrrr]maybe_convert_dtyperr.r1r0r infer_dtyperrHrobjects_to_datetime64nsrIrr$rrr*_maybe_infer_tzrAr%r(rrrr%rrr&)rrIrrArrrrrr data_dtype inferred_tzutc_valsrjrJrJrKrs*                     rrrrc Cs|dvsJtj|dtjd}|j}|jrdnd} ztj|d||||||d\} } | j|j | d} Wnt yE} zt d | d } ~ ww| d urQ| d | fSt | rY| | fSt| rg|rc| | fSt| t| ) a Convert data to array of timestamps. Parameters ---------- data : np.ndarray[object] dayfirst : bool yearfirst : bool utc : bool, default False Whether to convert timezone-aware timestamps to UTC. errors : {'raise', 'ignore', 'coerce'} require_iso8601 : bool, default False allow_object : bool Whether to return an object-dtype ndarray instead of raising if the data contains more than one timezone. allow_mixed : bool, default False Interpret integers as timestamps when datetime objects are also present. Returns ------- result : ndarray np.int64 dtype if returned values represent UTC timestamps np.datetime64[ns] if returned values represent wall times object if mixed timezones inferred_tz : tzinfo or None Raises ------ ValueError : if data cannot be converted to datetimes )rignorecoerceF)rrIFCK)errorsutcrrrr)orderz"Out of bounds nanosecond timestampNr)rHrobject_flags f_contiguousr array_to_datetimeravelreshapeshape OverflowErrorrrr(r.r8) rrrrrrrrrrrj tz_parsederrrJrJrKrQs8 (   rcCst|ds ||fSt|jr4|t}d}|dur0t|dkr0tt|s0t j dt t d||fSt |js>t|jrGtd|jdt|jrPtd t|jrdt|jsdtj|tjd }d}||fS) at Convert data based on dtype conventions, issuing deprecation warnings or errors where appropriate. Parameters ---------- data : np.ndarray or pd.Index copy : bool tz : tzinfo or None, default None Returns ------- data : np.ndarray or pd.Index copy : bool Raises ------ TypeError : PeriodDType data is passed rIFNraThe behavior of DatetimeArray._from_sequence with a timezone-aware dtype and floating-dtype data is deprecated. In a future version, this data will be interpreted as nanosecond UTC timestamps instead of wall-times, matching the behavior with integer dtypes. To retain the old behavior, explicitly cast to 'datetime64[ns]' before passing the data to pandas. To get the future behavior, first cast to 'int64'.rzdtype z& cannot be converted to datetime64[ns]zFPassing PeriodDtype data is invalid. Use `data.to_timestamp()` insteadr)r4r-rIrr$rrrrArrrr!r2r&r8r/r,r*rHrr)rrrArJrJrKrs6     rrcCsB|dur|}|S|dur |St||std|d||S)aV If a timezone is inferred from data, check that it is compatible with the user-provided timezone, if any. Parameters ---------- tz : tzinfo or None inferred_tz : tzinfo or None Returns ------- tz : tzinfo or None Raises ------ TypeError : if both timezones are present but do not match Nzdata is already tz-aware z, unable to set specified tz: )rrr8)rArrJrJrKrs rcCs|durDt|}t|tdrd}t|t|tjr |tks(t|tjtfs0td|dt|ddrDt t|}tt |j d}|S)a Check that a dtype, if passed, represents either a numpy datetime64[ns] dtype or a pandas DatetimeTZDtype. Parameters ---------- dtype : object Returns ------- dtype : None, numpy.dtype, or DatetimeTZDtype Raises ------ ValueError : invalid dtype Notes ----- Unlike validate_tz_from_dtype, this does _not_ allow non-existent tz errors to go through NM8zhPassing in 'datetime64' dtype with no precision is not allowed. Please pass in 'datetime64[ns]' instead.zUnexpected value for 'dtype': 'z0'. Must be 'datetime64[ns]' or DatetimeTZDtype'.rAr) r3r+rHrIrrr$r4rr rtz_standardizerA)rImsgrJrJrKr s     rcCs|durJt|trzt|}Wn tyYnwt|dd}|dur4|dur2t||s2td|}|durJt |rJ|durJt||sJtd|S)aU If the given dtype is a DatetimeTZDtype, extract the implied tzinfo object from it and check that it does not conflict with the given tz. Parameters ---------- dtype : dtype, str tz : None, tzinfo Returns ------- tz : consensus tzinfo Raises ------ ValueError : on tzinfo mismatch NrAz-cannot supply both a tz and a dtype with a tzzHcannot supply both a tz and a timezone-naive dtype (i.e. datetime64[ns])) rrDr4construct_from_stringr8rrrrr()rIrAdtzrJrJrKr> s$   rrOrrPc Cszt||}Wnty}ztd|d}~wwt|}t|}|dur8|dur8t||s6td|S|dur>|}|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_tzinfoAssertionErrorr8rAr)rOrPrArrrJrJrKrm s&   rTimestamp | NonercCsbd}|dur|r|}d}n|o|tk}|dur,|r$|}d}n|o+|tk}|||fS)NT)rr _midnight)rOrPrrrJrJrKr s rcCsZ|dur+|dur+|dkr|nd}||dd}t|ts|dur#||d<|jdi|}|S)a Localize a start or end Timestamp to the timezone of the corresponding start or end Timestamp Parameters ---------- ts : start or end Timestamp to potentially localize is_none : argument that should be None is_not_none : argument that should not be None freq : Tick, DateOffset, or None tz : str, timezone object or None ambiguous: str, localization behavior for ambiguous times nonexistent: str, localization behavior for nonexistent times Returns ------- ts : Timestamp NrIF)rrrArArJ)rr;r)r is_none is_not_noner]rArr localize_argsrJrJrKr s rccslt|}t|}|tur|nd}t|}|tur|nd}|r*||s*||}n |r6||s6||}|durG||krG|jdkrGd}d}|durS||d|}|dur_||d|}|}|jdkr||kr|V||krsdS||}||krtd|d|}||ksjdSdS||kr|V||krdS||}||krtd|d|}||ksdSdS)a Generates a sequence of dates corresponding to the specified time offset. Similar to dateutil.rrule except uses pandas DateOffset objects to represent time increments. Parameters ---------- start : datetime, (default None) end : datetime, (default None) periods : int, (default None) offset : DateOffset, (default BDay()) Notes ----- * This method is faster for generating weekdays than dateutil.rrule * At least two of (start, end, periods) must be specified. * If both start and end are specified, the returned dates will satisfy start <= date <= end. Returns ------- dates : generator object NrrzOffset z did not increment datez did not decrement date) rrr is_on_offset rollforwardrollbackn_applyr)rOrPrrcur next_daterJrJrKr sJ     r)r@)rArBrCrDrx)rMrDrNrDr)rrrvrr)NFNFFr) rrrrrrrrrr)FrFFF)rrrrrrrr)rrrArB)rArBrrBrvrB)rArBrvrB)rOrrPrrArBrvrB)rOrrPrrr)g __future__rrrrrtypingrrr rnumpyrH pandas._libsr r pandas._libs.tslibsr r rrrrrrrrrrrrrrrrpandas._typingr pandas.errorsrr pandas.util._exceptionsr!pandas.util._validatorsr"pandas.core.dtypes.astyper#pandas.core.dtypes.commonr$r%r&r'r(r)r*r+r,r-r.r/r0r1r2r3pandas.core.dtypes.dtypesr4pandas.core.dtypes.missingr5rMr6rpandas.core.arrays._rangesr7pandas.core.commoncorecommonrpandas.tseries.frequenciesr8pandas.tseries.offsetsr9r:r;rer=r>r?rrLrq TimelikeOps DatelikeOpsrrrrrrrrrrrrrrJrJrJrKs P    H         ( "  V E 4 / +!