o 5c$@sddlmZddlmZddlmZddlmZddlmZddl m Z m Z m Z m Z mZmZmZmZmZddlZddlZddlmZdd lmZmZmZmZmZmZmZdd l m!Z!m"Z"m#Z#dd l$m%Z%dd l&m'Z'm(Z(m)Z)m*Z*m+Z+dd l,m-Z-ddl.m/Z/m0Z0m1Z1m2Z2m3Z3m4Z4m5Z5m6Z6m7Z7m8Z8ddl9m:Z:m;Z;ddlm?Z?m@Z@ddlAmBZBddlCmDZDddlEmFZFddlGmHZHmIZImJZJddlKmLZLddlMmNZNddlOmPZPe rddlQmRZRddlSmTZTddlUmVZVmWZWee ee'fZXeeYeZfZ[ee[efZ\ee\eXfZ]ee e[ee[dfe'fZ^Gdddedd Z_Gd!d"d"e_d#d Z`ee`d$fZad%Zbddd(d)Zc ddd3d4Zddd;d<Ze dddCdDZf dddHdIZgddLdMZh    N #   dddWdXZidd[d\Zjdd]d^Zkdd_d`ZldadbZme          dddfdgZne          dddidgZne          dddldgZn N # #     # m dddpdgZnidqdqdrdqdsdsdtdsdududvdudwdxdydxdzd{d|d{d}d~dd~dddddddddddddddZodddZpdddZqdddZrgdZsdS)) annotations)abc)datetime)partial)islice) TYPE_CHECKINGCallableHashableListTuple TypedDictUnioncastoverloadN)tslib)OutOfBoundsDatetime Timedelta TimestampiNaT nat_stringsparsing timezones)DateParseError format_is_isoguess_datetime_format)array_strptime) AnyArrayLike ArrayLikeDateTimeErrorChoicesTimezonenpt)find_stack_level) ensure_objectis_datetime64_dtypeis_datetime64_ns_dtypeis_datetime64tz_dtypeis_float is_integeris_integer_dtype is_list_likeis_numeric_dtype is_scalar) ABCDataFrame ABCSeries)notna) DatetimeArray IntegerArray) algorithms)unique)ExtensionArray)maybe_convert_dtypeobjects_to_datetime64ns tz_to_dtype) extract_array)Index) DatetimeIndex)NaTType) UnitChoices) DataFrameSeries.c@s&eZdZUded<ded<ded<dS)YearMonthDayDictDatetimeDictArgyearmonthdayN__name__ __module__ __qualname____annotations__rHrHQ/var/www/html/gps/gps/lib/python3.10/site-packages/pandas/core/tools/datetimes.pyr>fs  r>T)totalc@sVeZdZUded<ded<ded<ded<ded<ded<ded<ded <ded <d S) FulldatetimeDictr?hourhoursminuteminutessecondsecondsmsusnsNrCrHrHrHrIrKls  rKFr<2dayfirst bool | NonecCs0t|d}t|rt||d|dSdS)NrrV)r.nonzerolenr)arrrVnon_nan_elementsrHrHrI _guess_datetime_format_for_arraysr]ffffff?argArrayConvertible unique_sharefloat check_count int | NonereturnboolcCsd}|durt|tkrdSt|dkrt|d}nd}nd|kr-t|ks2JdJd|dkr8dSd|krEd ksJJd Jd z tt||}Wn ty]YdSwt|||krhd}|S) a Decides whether to do caching. If the percent of unique elements among `check_count` elements less than `unique_share * 100` then we can do caching. Parameters ---------- arg: listlike, tuple, 1-d array, Series unique_share: float, default=0.7, optional 0 < unique_share < 1 check_count: int, optional 0 <= check_count <= len(arg) Returns ------- do_caching: bool Notes ----- By default for a sequence of less than 50 items in size, we don't do caching; for the number of elements less than 5000, we take ten percent of all elements to check for a uniqueness share; if the sequence size is more than 5000, then we check only the first 500 elements. All constants were chosen empirically by. TNFi irz1check_count must be in next bounds: [0; len(arg)]z+unique_share must be in next bounds: (0; 1))rZstart_caching_atsetr TypeError)r_rarc do_cachingunique_elementsrHrHrI should_caches4  $ rnformat str | Nonecacheconvert_listlikerr=cCsddlm}|td}|rDt|s|St|}t|t|krD|||}z|||d}Wn ty7|YSw|jjsD||j }|S)a Create a cache of unique dates from an array of dates Parameters ---------- arg : listlike, tuple, 1-d array, Series format : string Strftime format to parse time cache : bool True attempts to create a cache of converted values convert_listlike : function Conversion function to apply on dates Returns ------- cache_array : Series Cache of converted, unique dates. Can be empty rr=dtype)index) pandasr=objectrnr2rZrrv is_unique duplicated)r_rorqrrr= cache_array unique_dates cache_datesrHrHrI _maybe_caches    r~dt_arrayrutcnamer r8cCs2t|r|rdnd}t|||dSt|||jdS)a Properly boxes the ndarray of datetimes to DatetimeIndex if it is possible or to generic Index instead Parameters ---------- dt_array: 1-d array Array of datetimes to be wrapped in an Index. tz : object None or 'utc' name : string, default None Name for a resulting index Returns ------- result : datetime of converted dates - DatetimeIndex if convertible to sole datetime64 type - general Index otherwise rNtzr)rru)r#r9r8ru)rrrrrHrHrI_box_as_indexlikes r DatetimeScalarOrArrayConvertibler{Hashable | NonecCs*ddlm}|||}t|jd|dS)a Convert array of dates with a cache and wrap the result in an Index. Parameters ---------- arg : integer, float, string, datetime, list, tuple, 1-d array, Series cache_array : Series Cache of converted, unique dates name : string, default None Name for a DatetimeIndex Returns ------- result : Index-like of converted dates rrsNrr)rwr=mapr_values)r_r{rr=resultrHrHrI_convert_and_box_caches rr np.ndarraycsFtddt||D}durtfdd|D}t||dS)a Return results from array_strptime if a %z or %Z directive was passed. Parameters ---------- result : ndarray[int64] int64 date representations of the dates timezones : ndarray pytz timezone objects tz : object None or pytz timezone object name : string, default None Name for a DatetimeIndex Returns ------- tz_result : Index-like of parsed dates with timezone cSsg|] \}}t||qSrH)r tz_localize).0reszonerHrHrI <sz3_return_parsed_timezone_results..Ncsg|]}|qSrH) tz_convert)r tz_resultrrHrIr@r)nparrayzipr8)rrrr tz_resultsrHrrI_return_parsed_timezone_results(s  rraiserTimezone | Noneuniterrorsstrinfer_datetime_format yearfirstexactc  CsRt|ttfrtj|dd}t|dd} t| r4t|ttfs&t|||dS|dkr2| d |}|St | r[t|ttfsRzt|||dWSt yQY|Sw|rY| |S|S|duro|durgt dt |||||St|dd d kr{td |} zt|d t|d \}} Wn/ty|d krtjdgddt|} t| |dYS|dkrt||d}|YSwt|}d }|r|durt||d}|durt|}|r| }d}|durt|| |||| ||}|dur|S|dus|sJ|dk}t||||||dd\}}|durt|t|d}tj||dS|dk}t|||dS)aS Helper function for to_datetime. Performs the conversions of 1D listlike of dates Parameters ---------- arg : list, tuple, ndarray, Series, Index date to be parsed name : object None or string for the Index name tz : object None or 'utc' unit : str None or string of the frequency of the passed data errors : str error handing behaviors from to_datetime, 'raise', 'coerce', 'ignore' infer_datetime_format : bool, default False inferring format behavior from to_datetime dayfirst : bool dayfirst parsing behavior from to_datetime yearfirst : bool yearfirst parsing behavior from to_datetime exact : bool, default True exact format matching behavior from to_datetime Returns ------- Index-like of parsed dates OrtruNrrz#cannot specify both format and unitndimrhzAarg must be a string, datetime, list, tuple, 1-d array, or SeriesF)copyrcoerceNaTzdatetime64[ns]rignorerXT)rVrrrrequire_iso8601 allow_objectr) isinstancelisttuplerrgetattrr%r/r9rrr$ ValueError_to_datetime_with_unitrkr4r maybe_get_tzrepeatrZr8r"r]r_to_datetime_with_formatr5r6 _simple_newr)r_rorrrrrrVrr arg_dtypeorig_arg_npvaluesidxrformat_is_iso8601rrr tz_parseddtarHrHrI_convert_listlike_datetimesEs)         rfmt Index | Nonec Cs|dk}z t||||d\}} WnSty7|dkr|dkr3tj|jdd}|d} | tn|}Yn<tyc|s^|dkrD|dkr[tj|jdd}|d} | tn|}nYdSYnwd |vsld |vrst || ||St |||d S) zL Call array_strptime, with fallback behavior depending on 'errors'. r)rrrrM8[ns]rti8Nz%Zz%zr) rrremptyshapeviewfillrrrr) r_rrrrrrrrriresultrHrHrI_array_strptime_with_fallbacks6       rc Csd}|dkr6t|}zt||d}Wntttfy&} ztd| d} ~ ww|dur6|dk} t|| |dSt|||||||} | S)zG Try parsing with the given format, returning None on failure. N%Y%m%drz0cannot convert the input to '%Y%m%d' date formatrr)r"_attempt_YYYYMMDDrrkrrr) r_rrrrrrrrerrrrrHrHrIrs( rcCst|dd}t|tr|d|d}d}nt|}tj|||d\}}|dkr2tj ||d}nt ||d}t|t s?|S| d  |}|dur\|j durW| |}|S| |}|S) zF to_datetime specalized to the case where a 'unit' is passed. T) extract_numpyz datetime64[]NrrrUTC)r7rr0astyperasarrayrarray_with_unit_to_datetimer8 _with_inferr9rrr)r_rrrrr[rrrHrHrIr s$        rc Cs|dkrQ|}td}|dkrtdz||}Wnty+}ztd|d}~wwtj|}tj|}t||ksHt||krOt|d|St |r]t |sot |sot t |sotd|d |d zt|}Wn)ty}z td |d |d}~wty}z td |d |d}~ww|jdurtd|d|td} | td|d} t|rt|tttjfst |}|| }|S)a Helper function for to_datetime. Adjust input argument to the specified origin Parameters ---------- arg : list, tuple, ndarray, Series, Index date to be adjusted origin : 'julian' or Timestamp origin offset for the arg unit : str passed unit from to_datetime, must be 'D' Returns ------- ndarray or scalar of adjusted date(s) julianrDz$unit must be 'D' for origin='julian'z3incompatible 'arg' type for given 'origin'='julian'Nz% is Out of Bounds for origin='julian''z!' is not compatible with origin='z+'; it must be numeric with a unit specifiedzorigin z is Out of Boundsz# cannot be converted to a Timestampzorigin offset z must be tz-naiverh)r)rto_julian_daterrkmaxminranyrr+r'r&r*rrrr)rr-r8ndarray) r_originroriginalj0rj_maxj_minoffset td_offsetioffsetrHrHrI_adjust_to_originEsl  $      rDatetimeScalarrrc CdSNrH r_rrVrrrorrrrrqrHrHrI to_datetimerSeries | DictConvertiblec CrrrHrrHrHrIrr list | tuple | Index | ArrayLiker9c CrrrHrrHrHrIrrunix2DatetimeScalarOrArrayConvertible | DictConvertible8DatetimeIndex | Series | DatetimeScalar | NaTType | Nonec  Cs|durdS| dkrt|| |}|rdnd} tt| ||||||d} t|tr@|} | dur>|jdur9|| } | S|| } | St|trht ||| | }|j sV| |} | S| |j |}|j ||j|jd} | St|ttjfrxt||| } | St|trt ||| | }|j st|||jd} | S| |||jd} | St|rzttttttjdtf|}t ||| | }Wnty|dkr‚d d lm }|gt!d }Ynw|j st||} | S| ||} | S| t"|g|d } t|t#rt| tj$rt#| } | S) uF7 Convert argument to datetime. This function converts a scalar, array-like, :class:`Series` or :class:`DataFrame`/dict-like to a pandas datetime object. Parameters ---------- arg : int, float, str, datetime, list, tuple, 1-d array, Series, DataFrame/dict-like The object to convert to a datetime. If a :class:`DataFrame` is provided, the method expects minimally the following columns: :const:`"year"`, :const:`"month"`, :const:`"day"`. errors : {'ignore', 'raise', 'coerce'}, default 'raise' - If :const:`'raise'`, then invalid parsing will raise an exception. - If :const:`'coerce'`, then invalid parsing will be set as :const:`NaT`. - If :const:`'ignore'`, then invalid parsing will return the input. dayfirst : bool, default False Specify a date parse order if `arg` is str or is list-like. If :const:`True`, parses dates with the day first, e.g. :const:`"10/11/12"` is parsed as :const:`2012-11-10`. .. warning:: ``dayfirst=True`` is not strict, but will prefer to parse with day first. If a delimited date string cannot be parsed in accordance with the given `dayfirst` option, e.g. ``to_datetime(['31-12-2021'])``, then a warning will be shown. yearfirst : bool, default False Specify a date parse order if `arg` is str or is list-like. - If :const:`True` parses dates with the year first, e.g. :const:`"10/11/12"` is parsed as :const:`2010-11-12`. - If both `dayfirst` and `yearfirst` are :const:`True`, `yearfirst` is preceded (same as :mod:`dateutil`). .. warning:: ``yearfirst=True`` is not strict, but will prefer to parse with year first. utc : bool, default None Control timezone-related parsing, localization and conversion. - If :const:`True`, the function *always* returns a timezone-aware UTC-localized :class:`Timestamp`, :class:`Series` or :class:`DatetimeIndex`. To do this, timezone-naive inputs are *localized* as UTC, while timezone-aware inputs are *converted* to UTC. - If :const:`False` (default), inputs will not be coerced to UTC. Timezone-naive inputs will remain naive, while timezone-aware ones will keep their time offsets. Limitations exist for mixed offsets (typically, daylight savings), see :ref:`Examples ` section for details. See also: pandas general documentation about `timezone conversion and localization `_. format : str, default None The strftime to parse time, e.g. :const:`"%d/%m/%Y"`. Note that :const:`"%f"` will parse all the way up to nanoseconds. See `strftime documentation `_ for more information on choices. exact : bool, default True Control how `format` is used: - If :const:`True`, require an exact `format` match. - If :const:`False`, allow the `format` to match anywhere in the target string. unit : str, default 'ns' The unit of the arg (D,s,ms,us,ns) denote the unit, which is an integer or float number. This will be based off the origin. Example, with ``unit='ms'`` and ``origin='unix'``, this would calculate the number of milliseconds to the unix epoch start. infer_datetime_format : bool, default False If :const:`True` and no `format` is given, attempt to infer the format of the datetime strings based on the first non-NaN element, and if it can be inferred, switch to a faster method of parsing them. In some cases this can increase the parsing speed by ~5-10x. origin : scalar, default 'unix' Define the reference date. The numeric values would be parsed as number of units (defined by `unit`) since this reference date. - If :const:`'unix'` (or POSIX) time; origin is set to 1970-01-01. - If :const:`'julian'`, unit must be :const:`'D'`, and origin is set to beginning of Julian Calendar. Julian day number :const:`0` is assigned to the day starting at noon on January 1, 4713 BC. - If Timestamp convertible, origin is set to Timestamp identified by origin. cache : bool, default True If :const:`True`, use a cache of unique, converted dates to apply the datetime conversion. May produce significant speed-up when parsing duplicate date strings, especially ones with timezone offsets. The cache is only used when there are at least 50 values. The presence of out-of-bounds values will render the cache unusable and may slow down parsing. .. versionchanged:: 0.25.0 changed default value from :const:`False` to :const:`True`. Returns ------- datetime If parsing succeeded. Return type depends on input (types in parenthesis correspond to fallback in case of unsuccessful timezone or out-of-range timestamp parsing): - scalar: :class:`Timestamp` (or :class:`datetime.datetime`) - array-like: :class:`DatetimeIndex` (or :class:`Series` with :class:`object` dtype containing :class:`datetime.datetime`) - Series: :class:`Series` of :class:`datetime64` dtype (or :class:`Series` of :class:`object` dtype containing :class:`datetime.datetime`) - DataFrame: :class:`Series` of :class:`datetime64` dtype (or :class:`Series` of :class:`object` dtype containing :class:`datetime.datetime`) Raises ------ ParserError When parsing a date from string fails. ValueError When another datetime conversion error happens. For example when one of 'year', 'month', day' columns is missing in a :class:`DataFrame`, or when a Timezone-aware :class:`datetime.datetime` is found in an array-like of mixed time offsets, and ``utc=False``. See Also -------- DataFrame.astype : Cast argument to a specified dtype. to_timedelta : Convert argument to timedelta. convert_dtypes : Convert dtypes. Notes ----- Many input types are supported, and lead to different output types: - **scalars** can be int, float, str, datetime object (from stdlib :mod:`datetime` module or :mod:`numpy`). They are converted to :class:`Timestamp` when possible, otherwise they are converted to :class:`datetime.datetime`. None/NaN/null scalars are converted to :const:`NaT`. - **array-like** can contain int, float, str, datetime objects. They are converted to :class:`DatetimeIndex` when possible, otherwise they are converted to :class:`Index` with :class:`object` dtype, containing :class:`datetime.datetime`. None/NaN/null entries are converted to :const:`NaT` in both cases. - **Series** are converted to :class:`Series` with :class:`datetime64` dtype when possible, otherwise they are converted to :class:`Series` with :class:`object` dtype, containing :class:`datetime.datetime`. None/NaN/null entries are converted to :const:`NaT` in both cases. - **DataFrame/dict-like** are converted to :class:`Series` with :class:`datetime64` dtype. For each row a datetime is created from assembling the various dataframe columns. Column keys can be common abbreviations like [‘year’, ‘month’, ‘day’, ‘minute’, ‘second’, ‘ms’, ‘us’, ‘ns’]) or plurals of the same. The following causes are responsible for :class:`datetime.datetime` objects being returned (possibly inside an :class:`Index` or a :class:`Series` with :class:`object` dtype) instead of a proper pandas designated type (:class:`Timestamp`, :class:`DatetimeIndex` or :class:`Series` with :class:`datetime64` dtype): - when any input element is before :const:`Timestamp.min` or after :const:`Timestamp.max`, see `timestamp limitations `_. - when ``utc=False`` (default) and the input is an array-like or :class:`Series` containing mixed naive/aware datetime, or aware with mixed time offsets. Note that this happens in the (quite frequent) situation when the timezone has a daylight savings policy. In that case you may wish to use ``utc=True``. Examples -------- **Handling various input formats** Assembling a datetime from multiple columns of a :class:`DataFrame`. The keys can be common abbreviations like ['year', 'month', 'day', 'minute', 'second', 'ms', 'us', 'ns']) or plurals of the same >>> df = pd.DataFrame({'year': [2015, 2016], ... 'month': [2, 3], ... 'day': [4, 5]}) >>> pd.to_datetime(df) 0 2015-02-04 1 2016-03-05 dtype: datetime64[ns] Passing ``infer_datetime_format=True`` can often-times speedup a parsing if its not an ISO8601 format exactly, but in a regular format. >>> s = pd.Series(['3/11/2000', '3/12/2000', '3/13/2000'] * 1000) >>> s.head() 0 3/11/2000 1 3/12/2000 2 3/13/2000 3 3/11/2000 4 3/12/2000 dtype: object >>> %timeit pd.to_datetime(s, infer_datetime_format=True) # doctest: +SKIP 100 loops, best of 3: 10.4 ms per loop >>> %timeit pd.to_datetime(s, infer_datetime_format=False) # doctest: +SKIP 1 loop, best of 3: 471 ms per loop Using a unix epoch time >>> pd.to_datetime(1490195805, unit='s') Timestamp('2017-03-22 15:16:45') >>> pd.to_datetime(1490195805433502912, unit='ns') Timestamp('2017-03-22 15:16:45.433502912') .. warning:: For float arg, precision rounding might happen. To prevent unexpected behavior use a fixed-width exact type. Using a non-unix epoch origin >>> pd.to_datetime([1, 2, 3], unit='D', ... origin=pd.Timestamp('1960-01-01')) DatetimeIndex(['1960-01-02', '1960-01-03', '1960-01-04'], dtype='datetime64[ns]', freq=None) **Non-convertible date/times** If a date does not meet the `timestamp limitations `_, passing ``errors='ignore'`` will return the original input instead of raising any exception. Passing ``errors='coerce'`` will force an out-of-bounds date to :const:`NaT`, in addition to forcing non-dates (or non-parseable dates) to :const:`NaT`. >>> pd.to_datetime('13000101', format='%Y%m%d', errors='ignore') datetime.datetime(1300, 1, 1, 0, 0) >>> pd.to_datetime('13000101', format='%Y%m%d', errors='coerce') NaT .. _to_datetime_tz_examples: **Timezones and time offsets** The default behaviour (``utc=False``) is as follows: - Timezone-naive inputs are converted to timezone-naive :class:`DatetimeIndex`: >>> pd.to_datetime(['2018-10-26 12:00', '2018-10-26 13:00:15']) DatetimeIndex(['2018-10-26 12:00:00', '2018-10-26 13:00:15'], dtype='datetime64[ns]', freq=None) - Timezone-aware inputs *with constant time offset* are converted to timezone-aware :class:`DatetimeIndex`: >>> pd.to_datetime(['2018-10-26 12:00 -0500', '2018-10-26 13:00 -0500']) DatetimeIndex(['2018-10-26 12:00:00-05:00', '2018-10-26 13:00:00-05:00'], dtype='datetime64[ns, pytz.FixedOffset(-300)]', freq=None) - However, timezone-aware inputs *with mixed time offsets* (for example issued from a timezone with daylight savings, such as Europe/Paris) are **not successfully converted** to a :class:`DatetimeIndex`. Instead a simple :class:`Index` containing :class:`datetime.datetime` objects is returned: >>> pd.to_datetime(['2020-10-25 02:00 +0200', '2020-10-25 04:00 +0100']) Index([2020-10-25 02:00:00+02:00, 2020-10-25 04:00:00+01:00], dtype='object') - A mix of timezone-aware and timezone-naive inputs is converted to a timezone-aware :class:`DatetimeIndex` if the offsets of the timezone-aware are constant: >>> from datetime import datetime >>> pd.to_datetime(["2020-01-01 01:00 -01:00", datetime(2020, 1, 1, 3, 0)]) DatetimeIndex(['2020-01-01 01:00:00-01:00', '2020-01-01 02:00:00-01:00'], dtype='datetime64[ns, pytz.FixedOffset(-60)]', freq=None) | Setting ``utc=True`` solves most of the above issues: - Timezone-naive inputs are *localized* as UTC >>> pd.to_datetime(['2018-10-26 12:00', '2018-10-26 13:00'], utc=True) DatetimeIndex(['2018-10-26 12:00:00+00:00', '2018-10-26 13:00:00+00:00'], dtype='datetime64[ns, UTC]', freq=None) - Timezone-aware inputs are *converted* to UTC (the output represents the exact same datetime, but viewed from the UTC time offset `+00:00`). >>> pd.to_datetime(['2018-10-26 12:00 -0530', '2018-10-26 12:00 -0500'], ... utc=True) DatetimeIndex(['2018-10-26 17:30:00+00:00', '2018-10-26 17:00:00+00:00'], dtype='datetime64[ns, UTC]', freq=None) - Inputs can contain both naive and aware, string or datetime, the above rules still apply >>> from datetime import timezone, timedelta >>> pd.to_datetime(['2018-10-26 12:00', '2018-10-26 12:00 -0530', ... datetime(2020, 1, 1, 18), ... datetime(2020, 1, 1, 18, ... tzinfo=timezone(-timedelta(hours=1)))], ... utc=True) DatetimeIndex(['2018-10-26 12:00:00+00:00', '2018-10-26 17:30:00+00:00', '2020-01-01 18:00:00+00:00', '2020-01-01 19:00:00+00:00'], dtype='datetime64[ns, UTC]', freq=None) Nrr)rrrVrrrr)rvrrr=rrrsrt)%rrrrrrrrr-r~rrr _constructorrvrr,rMutableMapping_assemble_from_unit_mappingsr8rr)rr rrr3rrrrwr=rxrrfbool_)r_rrVrrrorrrrrqrrrrr{valuesargcr=rHrHrIrsM     1 /  + ( & "      r@yearsrAmonthsrBdaysrLhrMrNmrOrPsrQrR millisecond millisecondsrS microsecondrT) microsecondsrT nanosecond nanosecondsc sddlm}mm}||}|jjstdddfdd|D}dd|D}gd }t t |t |}t |rOd |} td | d t t |t t } t | rnd | } td | dfdd} | ||dd| ||dd| ||d} z t| d|d} Wnttfy}ztd||d}~wwgd}|D]8}||}|dur||vrz| || |||d7} Wqttfy}z td|d||d}~wwq| S)a assemble the unit specified fields from the arg (DataFrame) Return a Series for actual parsing Parameters ---------- arg : DataFrame errors : {'ignore', 'raise', 'coerce'}, default 'raise' - If :const:`'raise'`, then invalid parsing will raise an exception - If :const:`'coerce'`, then invalid parsing will be set as :const:`NaT` - If :const:`'ignore'`, then invalid parsing will return the input tz : None or 'utc' Returns ------- Series r)r< to_numeric to_timedeltaz#cannot assemble with duplicate keyscSs,|tvrt|S|tvrt|S|Sr) _unit_maplower)valuerHrHrIfs   z'_assemble_from_unit_mappings..fcsi|]}||qSrHrH)rk)rrHrI rz0_assemble_from_unit_mappings..cSsi|]\}}||qSrHrH)rrvrHrHrIr r)r@rArB,zNto assemble mappings requires at least that [year, month, day] be specified: [z ] is missingz9extra keys have been passed to the datetime assemblage: [rcs&|d}t|r|jddd}|S)Nrint64Fr)r(r)r)rrrHrIrs z,_assemble_from_unit_mappings..coercer@'rAdrBr)rorrzcannot assemble the datetimes: N)rrrrRrSrT)rrzcannot assemble the datetimes [z]: )rwr<rrcolumnsryrkeysitemssortedrjrZjoinrrrrkget)r_rrr<rrunit_revrequiredreq _requiredexcess_excessrrrunitsurrH)rrrrIrrsf      rnpt.NDArray[np.object_]np.ndarray | Nonec sfddfdd}z |tjWStttfy!Ynwz|tj}||t|WStttfy<Ynwzt |t t }|||WStttfyYYdSw)a* try to parse the YYYYMMDD/%Y%m%d format, try to deal with NaT-like, arg is a passed in as an object dtype, but could really be ints/strings with nan-like/or floats (e.g. with nan) Parameters ---------- arg : np.ndarray[object] errors : {'raise','ignore','coerce'} cs>|jtdd}t|d|dd|d}tj|ddS)NFr rrrr)rrxrtry_parse_year_month_dayrarray_to_datetime)cargparsedrrHrIcalcs z_attempt_YYYYMMDD..calccsRtj|jdd}|d}t||<||tjtj}|d||<|S)Nrrtr)rrrrrrfloat64r )r"maskrr masked_result)r$rHrIcalc_with_masks   z)_attempt_YYYYMMDD..calc_with_maskN) rrr r OverflowErrorrkr%r.r1isinrr)r_rr(r"r&rH)r$rrIrs(    rcCs,tjdttdddlm}|||||S)Nzz`to_time` has been moved, should be imported from pandas.core.tools.times. This alias will be removed in a future version.) stacklevelr)to_time)warningswarn FutureWarningr!pandas.core.tools.timesr,)r_roinfer_time_formatrr,rHrHrIr,s r,)rrnrr,)F)rVrW)r^N)r_r`rarbrcrdrerf) r_r`rorprqrfrrrrer=)NN)rrrrWrr rer8r)r_rr{r=rrrer8)rrrer8)NNNrFNNT)rorprr rrrrprrrrfrVrWrrWrrf) rrrrfrrrrfrer)rrrer8) ..........)r_rrrrVrfrrfrrWrorprrfrrprrfrqrfrer)r_rrrrVrfrrfrrWrorprrfrrprrfrqrfrer=)r_rrrrVrfrrfrrWrorprrfrrprrfrqrfrer9) rFFNNTNFrT)r_rrrrVrfrrfrrWrorprrfrrprrfrqrfrer)rr)r_rrrrer)NFr)t __future__r collectionsrr functoolsr itertoolsrtypingrrr r r r r rrr-numpyr pandas._libsrpandas._libs.tslibsrrrrrrrpandas._libs.tslibs.parsingrrrpandas._libs.tslibs.strptimerpandas._typingrrrrr pandas.util._exceptionsr!pandas.core.dtypes.commonr"r#r$r%r&r'r(r)r*r+pandas.core.dtypes.genericr,r-pandas.core.dtypes.missingr. pandas.arraysr/r0 pandas.corer1pandas.core.algorithmsr2pandas.core.arrays.baser3pandas.core.arrays.datetimesr4r5r6pandas.core.constructionr7pandas.core.indexes.baser8pandas.core.indexes.datetimesr9pandas._libs.tslibs.nattyper:pandas._libs.tslibs.timedeltasr;rwr<r=r`rbrScalarrrr?r>rKDictConvertiblerir]rnr~rrrrrrrrrrrrr,__all__rHrHrHrIs6    ,  $  0               <0      0 &%I        ] 6