o ҷh*@s*dZddlmZddlmZddlZddlmZm Z ddl m Z m Z m Z ddlmZddlmZmZdd lmZdd lmZdd lmZddlmmZdd lmZm Z dd 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)find_stack_level) is_scalar pandas_dtype) ABCSeries) datetimelike)TimedeltaArray)Indexmaybe_extract_name)DatetimeTimedeltaMixin) inherit_names)DtypeObj)__neg____pos____abs__ total_secondsroundfloorceilT)wrap) componentsto_pytimedeltasumstdmedian_format_native_typesc@seZdZUdZdZeZed"ddZde d<e j Z ed#d d Z d d e je jd d d fd$ddZd%ddZddZd&ddZd'ddZed(d d!Zd S))TimedeltaIndexa 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``. 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) >>> pd.TimedeltaIndex([1, 2, 4, 8], unit='D') TimedeltaIndex(['1 days', '2 days', '4 days', '8 days'], dtype='timedelta64[ns]', freq=None) We can also let pandas infer the frequency when possible. >>> pd.TimedeltaIndex(range(5), unit='D', freq='infer') TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'], dtype='timedelta64[ns]', freq='D') timedeltaindexreturntype[libindex.TimedeltaEngine]cCstjSN)libindexTimedeltaEngineselfr+Q/var/www/html/venv/lib/python3.10/site-packages/pandas/core/indexes/timedeltas.py _engine_typeszTimedeltaIndex._engine_typer_dataResolution | NonecCs|jjSr&)r._resolution_objr)r+r+r,r0szTimedeltaIndex._resolution_objNFcopyboolc Cs&|tjurtjd|jdttdt|||}t|r"| ||dvr*t d|dur2t |}t |t rR|tjurR|dusE||jkrR|rK|}|j||dSt |trs|tjurs|durs|dusi||jkrs|ro|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.) stacklevel>MYyzqUnits 'M', 'Y', and 'y' are no longer supported, as they do not represent unambiguous timedelta values durations.name)frequnitdtyper1)r8refs)r no_defaultwarningswarn__name__ FutureWarningr rr _raise_scalar_data_error ValueErrorr isinstancerr;r1 _simple_newr"_view_from_sequence_not_strictr r _references) clsdatar:r9closedr;r1r8tdarrr<r+r+r,__new__sJ       zTimedeltaIndex.__new__r;rcCs t|dS)zF Can we compare values of the given dtype to our own? m)r is_np_dtype)r*r;r+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,rVs   zTimedeltaIndex.get_loclabelstrcCst|}|dfSr&)r)r*rYparsedr+r+r,_parse_with_resoszTimedeltaIndex._parse_with_resor[rcCs,||j}|t|jtdd}||fS)Nns)rresolution_stringrr)r*resor[lboundrboundr+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$r/)r1r2)r;rr$r2)rYrZ)r[r)r$rZ)r@ __module__ __qualname____doc___typr _data_clspropertyr-__annotations__r_get_string_slicer0rr=rMrPrVr\rcrer+r+r+r,r"&s0 H   A  r")r:periods int | Noner: str | Noner$c CsL|dur t|||r d}t|\}}tj||||||d}tj||dS)aS 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 00:00:00', '100001 days 00:00:00', '200001 days 00:00:00'], dtype='timedelta64[s]', freq='100000D') ND)rKr:r7)comany_nonedtlmaybe_infer_freqr_generate_ranger"rE) startendrnr9r8rKr:_rLr+r+r,timedelta_rangesT rz)NNNNNN)rnror:rpr$r")*rh __future__rtypingrr> pandas._libsrr'rpandas._libs.tslibsrrrpandas.util._exceptionsr pandas.core.dtypes.commonr r pandas.core.dtypes.genericr pandas.core.arraysr rtpandas.core.arrays.timedeltasrpandas.core.commoncorecommonrrpandas.core.indexes.baserr pandas.core.indexes.datetimelikerpandas.core.indexes.extensionrpandas._typingr _field_opsr"rzr+r+r+r,sN          N