o 5c3@sddlmZddlmZddlmZmZddlZddl m Z m Z ddl m Z mZmZmZmZmZmZmZmZddlmZddlmZdd lmZmZmZdd lmZm Z m!Z!dd l"m#Z$dd l%m&Z&dd l'm(Z(ddl)m*Z*m+Z+m,Z,m-Z-m.Z.m/Z/m0Z0m1Z1m2Z2ddl3m4Z4ddl5m6Z6ddl7m8Z9ddl:m;Z;ddlZ?ddl@mAZAerddlBmCZCd-ddZDGddde9jEZF d.d/d#d$ZGd0d&d'ZHd1d(d)ZId2d+d,ZJdS)3) annotations) timedelta) TYPE_CHECKINGcastN)libtslibs) BaseOffsetNaTNaTTypeTick Timedeltaastype_overflowsafeiNaTperiods_per_second to_offset)precision_from_unit)get_timedelta_field)array_to_timedelta64ints_to_pytimedeltaparse_timedelta_unit)DtypeObjNpDtypenpt)function)validate_endpoints)astype_td64_unit_conversion) TD64NS_DTYPEis_dtype_equalis_float_dtypeis_integer_dtypeis_object_dtype is_scalaris_string_dtypeis_timedelta64_dtype pandas_dtype)isna)nanops) datetimelike)generate_regular_range)unpack_zerodim_and_defer DataFramenamestralias docstringcs*dfdd }||_d|d|_t|S)Nreturn np.ndarraycs0|j}t||jd}|jr|j|ddd}|S)Nresofloat64) fill_valueconvert)asi8r_reso_hasna_maybe_mask_results)selfvaluesresultr.S/var/www/html/gps/gps/lib/python3.10/site-packages/pandas/core/arrays/timedeltas.pyfBsz_field_accessor..f )r0r1)__name____doc__property)r,r.r/rAr?r>r@_field_accessorAs rFcseZdZUdZdZeddZeeje fZ e Z dZ eddd Zd ZgZd ed <gZd ed <dgZd ed<gdZd ed<eeeZd ed<gdZd ed<dddZedddZdZeZeddZedefdfd#d$ Zeed%d&dd)d*Z eed%e!j"dfdd+d,Z#edd-d.Z$ddd0d1Z%dd2d3Z&ddd5d6Z'ddd8d9Z(d:d;Z)dddd%dd7de?d|d|d}Z@e?d~d~dZAe?dddZBe?dddZCedddZDZES)TimedeltaArraya Pandas ExtensionArray for timedelta data. .. warning:: TimedeltaArray is currently experimental, and its API may change without warning. In particular, :attr:`TimedeltaArray.dtype` is expected to change to be an instance of an ``ExtensionDtype`` subclass. Parameters ---------- values : array-like The timedelta data. dtype : numpy.dtype Currently, only ``numpy.dtype("timedelta64[ns]")`` is accepted. freq : Offset, optional copy : bool, default False Whether to copy the underlying array of data. Attributes ---------- None Methods ------- None timedeltaarrayr ns)r timedelta64r0type[Timedelta]cCstSNr r;r?r?r@ _scalar_typevzTimedeltaArray._scalar_typeiz list[str] _other_ops _bool_opsfreq _object_ops)daysseconds microseconds nanoseconds _field_ops_datetimelike_ops)to_pytimedelta total_secondsroundfloorceil_datetimelike_methodsxnp.timedelta64Timedelta | NaTTypecCs(|d}|tjkr tStj||jdS)Ni8r2)viewr valuer _from_value_and_resor8)r;rayr?r?r@ _box_funcs  zTimedeltaArray._box_funcnp.dtypecCs|jjS)a3 The dtype for the TimedeltaArray. .. warning:: A future version of pandas will change dtype to be an instance of a :class:`pandas.api.extensions.ExtensionDtype` subclass, not a ``numpy.dtype``. Returns ------- numpy.dtype )_ndarraydtyperNr?r?r@rlszTimedeltaArray.dtypeNcCst|jt|}|SrL)_validate_td64_dtyperl)clsr<rlr?r?r@_validate_dtypes zTimedeltaArray._validate_dtyper<r1BaseOffset | Nonecsht|tjr |jdks Jt|rJt|tjs Jt|||jks'Jtj ||d}||_ |S)Nm)r<rl) isinstancenprlkindr is_unitlessndarraytypesuper _simple_new_freq)rnr<rSrlr= __class__r?r@ryszTimedeltaArray._simple_newFrlcopyr~boolcCsB|rt|t||dd\}}td|d\}}|j||j|dS)Nr~unitFrlrS)rmsequence_to_td64nsdtlvalidate_inferred_freqryrl)rndatarlr~ inferred_freqrS_r?r?r@_from_sequences zTimedeltaArray._from_sequencec Cs|rt||dvs J|du}|tjur|nd}t|\}}t|||d\}}t|||\}}|r6d}|j||j|d} |durO|durO| | || S|rWt | j | _ | S)NYrhMrr) rmr no_defaultrmaybe_infer_freqrrryrl_validate_frequencyrrrz) rnrrlr~rSr explicit_none freq_inferrr=r?r?r@_from_sequence_not_stricts"    z(TimedeltaArray._from_sequence_not_strictc Cst|}|durtdd|||fDrtdt||||dkr'td|dur/t|}|dur7t|}t|\}}|durIt||||}n t |j |j | d}|s]|dd}|se|dd}| d } |j| | j|d S) Ncss|]}|duVqdSrLr?.0rar?r?r@ sz1TimedeltaArray._generate_range..z1Must provide freq argument if no data is suppliedzVOf the four parameters: start, end, periods, and freq, exactly three must be specifiedrdm8[ns]r)rvalidate_periodsany ValueErrorcomcount_not_noner rr(rslinspacerfastypereryrl) rnstartendperiodsrSclosed left_closed right_closedindex td64valuesr?r?r@_generate_ranges*      zTimedeltaArray._generate_rangesetitemcCs8t||js|turtd|j||dt|jdS)Nz'value' should be a Timedelta.)rrI)rrrOr r_check_compatible_withrsrJrf)r;rfrr?r?r@ _unbox_scalarszTimedeltaArray._unbox_scalarcCst|SrLrM)r;rfr?r?r@_scalar_from_stringsz"TimedeltaArray._scalar_from_stringNonecCsdSrLr?)r;otherrr?r?r@r rPz%TimedeltaArray._check_compatible_withTcCs4t|}|jdkrt|j||dStjj|||dS)Nrqr~)r$rtrrkrDatetimeLikeArrayMixinr)r;rlr~r?r?r@r's zTimedeltaArray.astypec cs|jdkrtt|D]}||Vq dS|j}t|}d}||d}t|D]}||}t|d||}t|||dd}|EdHq)dS)Nri'Tbox)ndimrangelenrkminr) r;irlength chunksizechunksstart_iend_i convertedr?r?r@__iter__3s     zTimedeltaArray.__iter__r)axisrloutkeepdimsinitialskipna min_countr int | NonerlNpDtype | Nonerrrintc Cs6td||||dtj|j|||d}|||S)Nr?)rlrrr)rrr)nv validate_sumr&nansumrk_wrap_reduction_result) r;rrlrrrrrr=r?r?r@sumFs   zTimedeltaArray.sumr)rrlrddofrrrcCsRtjd|||dddtj|j|||d}|dus|jdkr$||S||S)Nr?)rlrrstd)fname)rrrr)rvalidate_stat_ddof_funcr&nanstdrkrri_from_backing_data)r;rrlrrrrr=r?r?r@rZs   zTimedeltaArray.stdboxedcCsddlm}||ddS)Nrget_format_timedelta64Tr)pandas.io.formats.formatr)r;rrr?r?r@ _formatterps  zTimedeltaArray._formatter)na_rep date_formatnpt.NDArray[np.object_]cKs,ddlm}||j|}t|dd|jS)Nrrr)rrrkrs frompyfunc)r;rrkwargsr formatterr?r?r@_format_native_typesus  z#TimedeltaArray._format_native_typescCs.t|trJtdt|jdt|j)Nzcannot add the type z to a )rrr TypeErrorrwrCr;rr?r?r@ _add_offsetszTimedeltaArray._add_offset__mul__cstr$|j}d}|jdurts|j}t|j||j|dStds.t t t |kr?t js?t dt jrd|jfddtt |D}t |}t|j||jdS|j}t|j||jdS)Nrrlz$Cannot multiply with unequal lengthscsg|] }||qSr?r?rnarrrr?r@ z*TimedeltaArray.__mul__..rl)r!rkrSr%rwryrlhasattrrsarrayrr#rr r)r;rr=rSr?rr@rs"       zTimedeltaArray.__mul__ __truediv__cst||jr't|}td|tur"tj|jtjd}| tj |S|j |St |rM|j |}d}|jdurB|jj|}t|}t|j||j|dSt|dsWt|}t|t|krctdt|jrm|j |St|jr||fddttD}t||j}t j|dd }|d kr|}t|||j}|S|d kr|t }|S|d kr|tj }|S|j |}t|j||jdS) Nrcrrrl*Cannot divide vectors with unequal lengthscsg|] }||qSr?r?roravsravr?r@rrz.TimedeltaArray.__truediv__..Frrfloatingdatetime)!rr_recognized_scalarsr rr rsemptyshaper4fillnanrkrr!rSdeltarrwryrlrrrrr#r ravelrreshape infer_dtyperrfloat)r;rr=rS result_listinferredflatr?rr@rsL                 zTimedeltaArray.__truediv__ __rtruediv__cstjr'ttdtur"tjjtjd}| tj |Sj St r.z data by )rrrr rr rsrrr4rrrkrr!rrwrCrrrrr#rlr r)r;rr=rr?rr@rs.          zTimedeltaArray.__rtruediv__ __floordiv__c s t|rRt||jr,t|}td|tur&tj|jtj d}| tj |S| |j S|j |}d}|jdurJ|j|}|jdkrJ|jjdkrJd}t|||dSt|ds\t|}t|t|krhtdt|jrt||}|j|j}|j|jB}|r|tj }t||tj |St|jr||fddttD}t|}t j!|d d }|"|j}|d krt#|\}}t||S|d kr|tj S|St$|jst%|jr|j |}t||St&|dt|j'} t(d | dt|j')NrcrrrSrl"Cannot divide with unequal lengthscsg|] }||qSr?r?rrr?r@rQrz/TimedeltaArray.__floordiv__..Frrrrr))r!rrrr rr rsrrr4rr __rfloordiv__rkrSnanosrwrrrrr#rlr7_isnanrrputmaskr rrasarrayrrrrrrgetattrrCr) r;rr=rSmaskres_list result_flatrrrlr?rr@rsZ                     zTimedeltaArray.__floordiv__r csZtr.)r!rrrr rr rsrrr4rrrrkrrwrCrrrrr#rlr7r rrr r rr)r;rr=rrrlr?rr@r is:            zTimedeltaArray.__rfloordiv____mod__cCs$t||jr t|}||||SrLrrrr rr?r?r@r zTimedeltaArray.__mod____rmod__cCs$t||jr t|}||||SrLrrr?r?r@rrzTimedeltaArray.__rmod__ __divmod__cCs0t||jr t|}||}|||}||fSrLrr;rres1res2r?r?r@r  zTimedeltaArray.__divmod__ __rdivmod__cCs0t||jr t|}||}|||}||fSrLrrr?r?r@rrzTimedeltaArray.__rdivmod__cCs2|jdurt||j |j dSt||j SNr)rSrwrkrNr?r?r@__neg__s zTimedeltaArray.__neg__cCst||j|jdSr)rwrkr~rSrNr?r?r@__pos__szTimedeltaArray.__pos__cCst|t|jSrL)rwrsabsrkrNr?r?r@__abs__szTimedeltaArray.__abs__npt.NDArray[np.float64]cCst|j}|j|j|ddS)a Return total duration of each element expressed in seconds. This method is available directly on TimedeltaArray, TimedeltaIndex and on Series containing timedelta values under the ``.dt`` namespace. Returns ------- seconds : [ndarray, Float64Index, Series] When the calling object is a TimedeltaArray, the return type is ndarray. When the calling object is a TimedeltaIndex, the return type is a Float64Index. When the calling object is a Series, the return type is Series of type `float64` whose index is the same as the original. See Also -------- datetime.timedelta.total_seconds : Standard library version of this method. TimedeltaIndex.components : Return a DataFrame with components of each Timedelta. Examples -------- **Series** >>> s = pd.Series(pd.to_timedelta(np.arange(5), unit='d')) >>> s 0 0 days 1 1 days 2 2 days 3 3 days 4 4 days dtype: timedelta64[ns] >>> s.dt.total_seconds() 0 0.0 1 86400.0 2 172800.0 3 259200.0 4 345600.0 dtype: float64 **TimedeltaIndex** >>> idx = pd.to_timedelta(np.arange(5), unit='d') >>> idx TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'], dtype='timedelta64[ns]', freq=None) >>> idx.total_seconds() Float64Index([0.0, 86400.0, 172800.0, 259200.0, 345600.0], dtype='float64') N)r5)rr8r:r7)r;ppsr?r?r@r\s 7zTimedeltaArray.total_secondscCs t|jS)z Return an ndarray of datetime.timedelta objects. Returns ------- timedeltas : ndarray[object] )rrkrNr?r?r@r[s zTimedeltaArray.to_pytimedeltarUz Number of days for each element.rVz>Number of seconds (>= 0 and less than 1 day) for each element.rWzFNumber of microseconds (>= 0 and less than 1 second) for each element.rXzJNumber of nanoseconds (>= 0 and less than 1 microsecond) for each element.r+cs`ddlm}gd|j}|rfddndd|fdd|Dd }|s.|d }|S) a" Return a DataFrame of the individual resolution components of the Timedeltas. The components (days, hours, minutes seconds, milliseconds, microseconds, nanoseconds) are returned as columns in a DataFrame. Returns ------- DataFrame rr*)rUhoursminutesrV millisecondsrWrXcst|r tjgtS|jSrL)r%rsrr componentsracolumnsr?r@rA9sz$TimedeltaArray.components..fcSs|jSrL)r&r'r?r?r@rA@scsg|]}|qSr?r?r)rAr?r@rCsz-TimedeltaArray.components..r(int64)pandasr+r9r)r;r+hasnansr=r?)r)rAr@r&s   zTimedeltaArray.components)r0rK)rarbr0rc)r0rj)r<r1rSrpr0rG)r~rr0rGrL)F)rrr0rb)r0rc)rrr0r)T)r~r) rrrlrrrrrrr) rrrlrrrrrrr)rr)r0r)r0rG)r0r!)r0r+)FrC __module__ __qualname__rD_typrsrJ_internal_fill_valuerr rr#_is_recognized_dtype_infer_matchesrErO__array_priority__rQ__annotations__rRrTrYrZr`rirlrzr_default_dtype classmethodroryrrrrrrrrrrrrrrrr)r__rmul__rrrr rrrrrrr r\r[rFrUrVrWrXr& __classcell__r?r?r{r@rGQs           $  $       E + L /        : rGFraiser~rr0tuple[np.ndarray, Tick | None]c Csf|dvsJd}|durt|}tj||dd\}}t|tr"|j}t|js,t|jr6t |||d}d}nit |jrIt ||d\}}|oG| }nVt |jrt |}t|pWd\}}|t j} || } |rmt | |} | || |t jd }t||<d}nt|jr|jtkrt|td }d}n td |jd t j||d }|jdksJ|||fS)a Parameters ---------- data : list-like copy : bool, default False unit : str, optional The timedelta unit to treat integers as multiples of. For numeric data this defaults to ``'ns'``. Must be un-specified if the data contains a str and ``errors=="raise"``. errors : {"raise", "coerce", "ignore"}, default "raise" How to handle elements that cannot be converted to timedelta64[ns]. See ``pandas.to_timedelta`` for details. Returns ------- converted : numpy.ndarray The sequence converted to a numpy array with dtype ``timedelta64[ns]``. inferred_freq : Tick or None The inferred frequency of the sequence. Raises ------ ValueError : Data cannot be converted to timedelta64[ns]. Notes ----- Unlike `pandas.to_timedelta`, if setting ``errors=ignore`` will not cause errors to be ignored; they are caught and subsequently ignored at a higher level. rNrG)cls_namererrorsF)rrItimedelta64[ns]rdtype ' cannot be converted to timedelta64[ns]rr)rr!ensure_arraylike_for_datetimelikerrrGrSr rlr"_objects_to_td64nsrints_to_td64nsrrsisnanrrr*r]rerr#rr rr) rr~rr=r copy_maderrqpbasefracr?r?r@rMsD !          rrIcCsxd}|dur|nd}|jtjkr|tj}d}|dkr3d|d}||}t|td}d}||fS|d}||fS) a Convert an ndarray with integer-dtype to timedelta64[ns] dtype, treating the integers as multiples of the given timedelta unit. Parameters ---------- data : numpy.ndarray with integer-dtype unit : str, default "ns" The timedelta unit to treat integers as multiples of. Returns ------- numpy.ndarray : timedelta64[ns] array converted from data bool : whether a copy was made FNrITz timedelta64[]rr>)rlrsr*rrer r)rrrE dtype_strr?r?r@rCs      rCcCs*tj|tjdd}t|||d}|dS)aR Convert a object-dtyped or string-dtyped array into an timedelta64[ns]-dtyped array. Parameters ---------- data : ndarray or Index unit : str, default "ns" The timedelta unit to treat integers as multiples of. Must not be specified if the data contains a str. errors : {"raise", "coerce", "ignore"}, default "raise" How to handle elements that cannot be converted to timedelta64[ns]. See ``pandas.to_timedelta`` for details. Returns ------- numpy.ndarray : timedelta64[ns] array converted from data Raises ------ ValueError : Data cannot be converted to timedelta64[ns]. Notes ----- Unlike `pandas.to_timedelta`, if setting `errors=ignore` will not cause errors to be ignored; they are caught and subsequently ignored at a higher level. Fr}r<r>)rsrobject_rre)rrr=r<r=r?r?r@rBs rBrcCsBt|}t|tdrd}t|t|tstd|d|S)NrJzhPassing in 'timedelta' dtype with no precision is not allowed. Please pass in 'timedelta64[ns]' instead.r?r@)r$rrsrlrr)rlmsgr?r?r@rms rm)r,r-r.r-r/r-)FNr9)r~rr0r:)rI)Nr9)r0r)K __future__rrrtypingrrnumpyrs pandas._libsrrpandas._libs.tslibsrr r r r r rrrpandas._libs.tslibs.conversionrpandas._libs.tslibs.fieldsrpandas._libs.tslibs.timedeltasrrrpandas._typingrrrpandas.compat.numpyrrpandas.util._validatorsrpandas.core.dtypes.astyperpandas.core.dtypes.commonrrrrr r!r"r#r$pandas.core.dtypes.missingr% pandas.corer&pandas.core.arraysr'rpandas.core.arrays._rangesr(pandas.core.commoncorecommonrpandas.core.ops.commonr)r+r+rF TimelikeOpsrGrrCrBrmr?r?r?r@sF ,    ,       W ($