o ŀg*@s*dZddlmZddlmZddlZddlmZm Z ddl m Z m Z m Z ddlmZddlmZdd lmZmZdd lmZdd lmZddlmmZdd lmZmZdd l m!Z!ddl"m#Z#eriddl$m%Z%e#gdej&edde#gdeGddde!Z'      ddddddZ(dS) z implement the TimedeltaIndex ) annotations) TYPE_CHECKINGN)indexlib) Resolution Timedelta to_offset)disallow_ambiguous_unit)find_stack_level) is_scalar pandas_dtype) ABCSeries)TimedeltaArray)Indexmaybe_extract_name)DatetimeTimedeltaMixin) inherit_names)DtypeObj)__neg____pos____abs__ total_secondsroundfloorceilT)wrap) componentsto_pytimedeltasumstdmedianc@seZdZUdZdZeZed"ddZde d<e j Z ed#d d Z d e je je jd d d fd$ddZd%ddZddZd&ddZd'ddZed(d d!Zd S))TimedeltaIndexal Immutable Index of timedelta64 data. Represented internally as int64, and scalars returned Timedelta objects. Parameters ---------- data : array-like (1-dimensional), optional Optional timedelta-like data to construct index with. unit : {'D', 'h', 'm', 's', 'ms', 'us', 'ns'}, optional The unit of ``data``. .. deprecated:: 2.2.0 Use ``pd.to_timedelta`` instead. freq : str or pandas offset object, optional One of pandas date offset strings or corresponding objects. The string ``'infer'`` can be passed in order to set the frequency of the index as the inferred frequency upon creation. dtype : numpy.dtype or str, default None Valid ``numpy`` dtypes are ``timedelta64[ns]``, ``timedelta64[us]``, ``timedelta64[ms]``, and ``timedelta64[s]``. copy : bool Make a copy of input array. name : object Name to be stored in the index. Attributes ---------- days seconds microseconds nanoseconds components inferred_freq Methods ------- to_pytimedelta to_series round floor ceil to_frame mean See Also -------- Index : The base pandas Index type. Timedelta : Represents a duration between two dates or times. DatetimeIndex : Index of datetime64 data. PeriodIndex : Index of Period data. timedelta_range : Create a fixed-frequency TimedeltaIndex. Notes ----- To learn more about the frequency strings, please see `this link `__. Examples -------- >>> pd.TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days']) TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'], dtype='timedelta64[ns]', freq=None) We can also let pandas infer the frequency when possible. >>> pd.TimedeltaIndex(np.arange(5) * 24 * 3600 * 1e9, freq='infer') TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'], dtype='timedelta64[ns]', freq='D') timedeltaindexreturntype[libindex.TimedeltaEngine]cCstjSN)libindexTimedeltaEngineselfr*R/var/www/html/myenv/lib/python3.10/site-packages/pandas/core/indexes/timedeltas.py _engine_typeszTimedeltaIndex._engine_typer_dataResolution | NonecCs|jjSr%)r-_resolution_objr(r*r*r+r/szTimedeltaIndex._resolution_objNFcopyboolc CsJ|tjurtjd|jdttd|tjur'tjd|jdttdnd}t|||}t|r8| |t ||durDt |}t |t rd|tjurd|dusW||jkrd|r]|}|j||dSt |tr|tjur|dur|dus{||jkr|r|S|St j|||||d}d} |st |ttfr|j} |j||| dS) NzThe 'closed' keyword in zD construction is deprecated and will be removed in a future version.) stacklevelzThe 'unit' keyword in za construction is deprecated and will be removed in a future version. Use pd.to_timedelta instead.name)frequnitdtyper0)r4refs)r no_defaultwarningswarn__name__ FutureWarningr rr _raise_scalar_data_errorr r isinstancerr7r0 _simple_newr!_view_from_sequence_not_strictr r _references) clsdatar6r5closedr7r0r4tdarrr8r*r*r+__new__sR         zTimedeltaIndex.__new__r7rcCs t|dS)zF Can we compare values of the given dtype to our own? m)r is_np_dtype)r)r7r*r*r+_is_comparable_dtypes z#TimedeltaIndex._is_comparable_dtypec CsN||z |jj|dd}Wnty }zt||d}~wwt||S)z Get integer location for requested label Returns ------- loc : int, slice, or ndarray[int] F)unboxN)_check_indexing_errorr-_validate_scalar TypeErrorKeyErrorrget_loc)r)keyerrr*r*r+rQs   zTimedeltaIndex.get_loclabelstrcCst|}|dfSr%)r)r)rTparsedr*r*r+_parse_with_resoszTimedeltaIndex._parse_with_resorVrcCs,||j}|t|jtdd}||fS)Nns)rresolution_stringrr)r)resorVlboundrboundr*r*r+_parsed_string_to_boundss z'TimedeltaIndex._parsed_string_to_boundscCsdS)N timedelta64r*r(r*r*r+ inferred_typeszTimedeltaIndex.inferred_type)r#r$)r#r.)r0r1)r7rr#r1)rTrU)rVr)r#rU)r< __module__ __qualname____doc___typr _data_clspropertyr,__annotations__r_get_string_slicer/rr9rHrKrQrWr^r`r*r*r*r+r!&s0 H   I  r!)r6periods int | Noner6 str | Noner#cCsF|dur t|||r d}t|}tj||||||d}tj||dS)a$ Return a fixed frequency TimedeltaIndex with day as the default. Parameters ---------- start : str or timedelta-like, default None Left bound for generating timedeltas. end : str or timedelta-like, default None Right bound for generating timedeltas. periods : int, default None Number of periods to generate. freq : str, Timedelta, datetime.timedelta, or DateOffset, default 'D' Frequency strings can have multiples, e.g. '5h'. name : str, default None Name of the resulting TimedeltaIndex. closed : str, default None Make the interval closed with respect to the given frequency to the 'left', 'right', or both sides (None). unit : str, default None Specify the desired resolution of the result. .. versionadded:: 2.0.0 Returns ------- TimedeltaIndex Notes ----- Of the four parameters ``start``, ``end``, ``periods``, and ``freq``, exactly three must be specified. If ``freq`` is omitted, the resulting ``TimedeltaIndex`` will have ``periods`` linearly spaced elements between ``start`` and ``end`` (closed on both sides). To learn more about the frequency strings, please see `this link `__. Examples -------- >>> pd.timedelta_range(start='1 day', periods=4) TimedeltaIndex(['1 days', '2 days', '3 days', '4 days'], dtype='timedelta64[ns]', freq='D') The ``closed`` parameter specifies which endpoint is included. The default behavior is to include both endpoints. >>> pd.timedelta_range(start='1 day', periods=4, closed='right') TimedeltaIndex(['2 days', '3 days', '4 days'], dtype='timedelta64[ns]', freq='D') The ``freq`` parameter specifies the frequency of the TimedeltaIndex. Only fixed frequencies can be passed, non-fixed frequencies such as 'M' (month end) will raise. >>> pd.timedelta_range(start='1 day', end='2 days', freq='6h') TimedeltaIndex(['1 days 00:00:00', '1 days 06:00:00', '1 days 12:00:00', '1 days 18:00:00', '2 days 00:00:00'], dtype='timedelta64[ns]', freq='6h') Specify ``start``, ``end``, and ``periods``; the frequency is generated automatically (linearly spaced). >>> pd.timedelta_range(start='1 day', end='5 days', periods=4) TimedeltaIndex(['1 days 00:00:00', '2 days 08:00:00', '3 days 16:00:00', '5 days 00:00:00'], dtype='timedelta64[ns]', freq=None) **Specify a unit** >>> pd.timedelta_range("1 Day", periods=3, freq="100000D", unit="s") TimedeltaIndex(['1 days', '100001 days', '200001 days'], dtype='timedelta64[s]', freq='100000D') ND)rFr6r3)comany_nonerr_generate_ranger!r@)startendrir5r4rFr6rGr*r*r+timedelta_range sS rr)NNNNNN)rirjr6rkr#r!))rc __future__rtypingrr: pandas._libsrr&rpandas._libs.tslibsrrrpandas._libs.tslibs.timedeltasr pandas.util._exceptionsr pandas.core.dtypes.commonr r pandas.core.dtypes.genericr pandas.core.arrays.timedeltasrpandas.core.commoncorecommonrmpandas.core.indexes.baserr pandas.core.indexes.datetimelikerpandas.core.indexes.extensionrpandas._typingr _field_opsr!rrr*r*r*r+sN          V