o ҷh@sddlmZddlmZddlZddlmZmZddlZddl Z ddl m Z m Z ddlmZmZmZmZmZmZmZmZmZmZmZmZddlmZddlmZmZdd l m!Z!m"Z"m#Z#m$Z$m%Z%dd l&m'Z(dd l)m*Z*dd l+m,Z,m-Z-m.Z.m/Z/m0Z0m1Z1m2Z2dd l3m4Z4ddl5m6Z6ddl7m8Z8m9Z9ddl:m;Z;ddlddl?m@Z@ddlAmBmCZDddlEmFZFerddlGmHZHddlImJZJmKZKmLZLmMZMmNZNmOZOddlPmQZQddlRZRd2ddZSGddde>jTZU   d3d4d'd(ZVd5d6d+d,ZWd7d8d-d.ZXd9d0d1ZYdS):) annotations) timedeltaN) TYPE_CHECKINGcast)libtslibs) NaTNaTTypeTick Timedeltaastype_overflowsafeget_supported_resoget_unit_from_dtypeiNaTis_supported_unitnpy_unit_to_abbrevperiods_per_second to_offset)precision_from_unit)get_timedelta_daysget_timedelta_field)array_to_timedelta64floordiv_object_arrayints_to_pytimedeltaparse_timedelta_unittruediv_object_array)function)validate_endpoints) TD64NS_DTYPEis_float_dtypeis_integer_dtypeis_object_dtype is_scalaris_string_dtype pandas_dtype)ExtensionDtype)isna)nanops roperator)datetimelike_accumulations) datetimelike)generate_regular_range)unpack_zerodim_and_defer)Iterator)AxisIntDateTimeErrorChoicesDtypeObjNpDtypeSelfnpt DataFramenamestralias docstringcs*dfdd }||_d|d|_t|S)Nreturn np.ndarraycsH|j}dkrt||jd}nt||jd}|jr"|j|ddd}|S)Ndaysresofloat64) fill_valueconvert)asi8r_cresor_hasna_maybe_mask_results)selfvaluesresultr8P/var/www/html/venv/lib/python3.10/site-packages/pandas/core/arrays/timedeltas.pyfVsz_field_accessor..f )r:r;)__name____doc__property)r6r8r9rLrJrIrK_field_accessorUsrQcs*eZdZUdZdZeddZeeje fZ ddZ dZ e dd d Zd ZgZd ed<gZd ed<dgZd ed<gdZd ed<eeedgZd ed<gdZd ed<dddZe dddZd ZeZed!d"Zed efdfd(d) Zed d*d+dd.d/Zed d*e j!d d0dd1d2Z"e dd d3dd5d6Z#dd7d8Z$dd9d:Z%dddDdEddMdNZ)d d d dOd*d>dPddRdSZ*d>dTdfdWdXZ+dddZd[Z,dd d\dd`daZ-dbdcZ.e/ddddedfZ0e0Z1dgdhZ2didjZ3ddldmZ4e/dndodpZ5e/dqdrdsZ6e/dtdudvZ7e/dwdxdyZ8e/dzd{d|Z9e/d}d~dZ:e/dddZ;e/dddZdddZ?dddZ@dddZAeBCdZDeEddeDZFeBCdZGeEddeGZHeBCdZIeEddeIZJeBCdZKeEddeKZLe dddZMZNS)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 Examples -------- >>> pd.arrays.TimedeltaArray(pd.TimedeltaIndex(['1H', '2H'])) ['0 days 01:00:00', '0 days 02:00:00'] Length: 2, dtype: timedelta64[ns] timedeltaarrayrnscCs t|dS)Nm)r is_np_dtypexrJrJrKs zTimedeltaArray.)r timedelta64r:type[Timedelta]cCstSNr rFrJrJrK _scalar_typezTimedeltaArray._scalar_typeiz list[str] _other_ops _bool_opsfreq _object_ops)r<seconds microseconds nanoseconds _field_opsunit_datetimelike_ops)to_pytimedelta total_secondsroundfloorceilas_unit_datetimelike_methodsrXnp.timedelta64Timedelta | NaTTypecCs(|d}|tjkr tStj||jdS)Ni8r=)viewr_valuer _from_value_and_resorC)rFrXyrJrJrK _box_funcs  zTimedeltaArray._box_funcnp.dtype[np.timedelta64]cCs|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 )_ndarraydtyper^rJrJrKr|szTimedeltaArray.dtypeNcCst|jt|}|Sr\)_validate_td64_dtyper|)clsrGr|rJrJrK_validate_dtypes zTimedeltaArray._validate_dtyperGnpt.NDArray[np.timedelta64] Tick | Noner|r2cstt|dsJt|rJt|tjsJt|||jks"J|dus-t|t s-Jt j ||d}||_ |S)NrU)rGr|) rrVr is_unitless isinstancenpndarraytyper|r super _simple_new_freq)r~rGrcr|rH __class__rJrKrszTimedeltaArray._simple_newFr|copyrboolcCsb|rt|}t||dd\}}td|d\}}td|}|dur(t||dd}|j||j|dS)NrriFrrr|rc)r}sequence_to_td64nsdtlvalidate_inferred_freqrr rr|)r~datar|r inferred_freqrc_rJrJrK_from_sequences zTimedeltaArray._from_sequence)r|rrcric Cs|rt|}|dvs J|du}|tjur|nd}t|\}}t|||d\}}t|||\}}td|}|r;d}|durFt||dd}|j ||j |d} |dur_|dur_| | || S|rgt | j | _| S)z] A non-strict version of _from_sequence, called from TimedeltaIndex.__new__. 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Supported resolutions are 's', 'ms', 'us', 'ns')r$rrVr|rrrr r{rrrcrrDatetimeLikeArrayMixinr)rFr|r res_valuesrJrJrKrhs    zTimedeltaArray.astyper-c cs|jdkrtt|D]}||Vq dS|j}t|}d}||d}t|D]}||}t|d||}t|||dd}|EdHq)dS)Nri'Tbox)ndimrangelenr{minr) rFirlength chunksizechunksstart_iend_i convertedrJrJrK__iter__s     zTimedeltaArray.__iter__r)axisr|outkeepdimsinitialskipna min_countrAxisInt | NoneNpDtype | Nonerrrintc Cs6td||||dtj|j|||d}|||S)NrJ)r|rrr)rrr)nv validate_sumr'nansumr{_wrap_reduction_result) rFrr|rrrrrrHrJrJrKsums   zTimedeltaArray.sumr)rr|rddofrrrcCsRtjd|||dddtj|j|||d}|dus|jdkr$||S||S)NrJ)r|rrstd)fname)rrrr)rvalidate_stat_ddof_funcr'nanstdr{rry_from_backing_data)rFrr|rrrrrHrJrJrKrs   zTimedeltaArray.std)rr6r7c sj|dkr!tt|}||jfd|i|}t|j|d|jdS|dkr)tdtj |fd|i|S)Ncumsumr)rcr|cumprodz$cumprod not supported for Timedelta.) getattrr)r{rrrr| TypeErrorr _accumulate)rFr6rkwargsoprHrrJrKrs zTimedeltaArray._accumulateboxedcCsddlm}||ddS)Nrget_format_timedelta64Tr)pandas.io.formats.formatr)rFrrrJrJrK _formatters  zTimedeltaArray._formatter)na_rep date_formatr str | floatnpt.NDArray[np.object_]cKs,ddlm}||j|}t|dd|jS)Nrrr)rrr{r frompyfunc)rFrrrr formatterrJrJrK_format_native_typess  z#TimedeltaArray._format_native_typescCs.t|trJtdt|jdt|j)Nzcannot add the type z to a )rr rrrNrrJrJrK _add_offsetszTimedeltaArray._add_offset__mul__cstr+|j}d}|jdur ts |j}|jdkr d}t|j||j|dStds5t t t |krHt jdsHtdtjrm|jfddtt |D}t |}t|j||jdS|j}t|j||jdS) Nrrr|rUz$Cannot multiply with unequal lengthscsg|] }||qSrJrJrnarrrrJrK z*TimedeltaArray.__mul__..r|)r"r{rcr&rrrr|hasattrrarrayrrrVrr!r)rFrrHrcrJrrKrs&        zTimedeltaArray.__mul__cCst||jr(t|}td|tur"tj|jtjd}| tj |S||j |S|t j t jfvr@tdt|jdt|j||j |}d}|jdur_|j|}|jdkr_|jjdkr_d}t|j||j|dS)zv Shared logic for __truediv__, __rtruediv__, __floordiv__, __rfloordiv__ with scalar 'other'. rsrzCannot divide z by Nrr)r_recognized_scalarsr rrremptyshaper?fillnanr{r(rtruediv rfloordivrrrNrcnanosrr|)rFrrresrHrcrJrJrK_scalar_divlike_op s$      z!TimedeltaArray._scalar_divlike_opcCs0t|ds t|}t|t|krtd|S)Nr|z*Cannot divide vectors with unequal lengths)r rr rrrrJrJrK_cast_divlike_op5s  zTimedeltaArray._cast_divlike_opnp.ndarray | SelfcCs||jt|}t|jst|jr%|tjtjfvr%t |j ||jdS|tjt j fvrG| t |B}|rG|tj}t||tj|S)z Shared logic for __truediv__, __floordiv__, and their reversed versions with timedelta64-dtype ndarray other. r)r{rasarrayr r|roperatortruedivfloordivrrr(rr&rrr?putmaskr)rFrrrHmaskrJrJrK_vector_divlike_op>s z!TimedeltaArray._vector_divlike_op __truediv__cCstj}t|r |||S||}t|jds#t|js#t |jr)| ||St |jrZt |}|jdkrRddt||D}dd|D}t j|dd}|St|j|}|StS)NrUrcSsg|]\}}||qSrJrJrleftrightrJrJrKrfz.TimedeltaArray.__truediv__..cSg|]}|ddqSrrreshaperrJrJrKrgr"rr)rrr"rrrrVr|r rrr!rrrzip concatenaterr{NotImplementedrFrrres_cols res_cols2rHrJrJrKrTs*        zTimedeltaArray.__truediv__ __rtruediv__csrtj}tr |Stjdr|St jr7fddt t D}t |StS)NrUcsg|] }||qSrJrJrrrFrJrKrrz/TimedeltaArray.__rtruediv__..)r(rr"rrrrVr|rr!rrrr r*)rFrr result_listrJr/rKr.qs     zTimedeltaArray.__rtruediv__ __floordiv__cCstj}t|r |||S||}t|jds#t|js#t |jr)| ||St |jr`t |}|jdkrQddt||D}dd|D}t j|dd}nt|j|}|jtks^J|StS)NrUrcSsg|]\}}||qSrJrJrrJrJrKrr"z/TimedeltaArray.__floordiv__..cSr#r$r%rrJrJrKrr"rr')rrr"rrrrVr|r rrr!rrrr(r)rr{objectr*r+rJrJrKr1s*        zTimedeltaArray.__floordiv__ __rfloordiv__csvtj}tr |Stjdr|St jr9fddt t D}t |}|StS)NrUcsg|] }||qSrJrJrr/rJrKrrz0TimedeltaArray.__rfloordiv__..)r(rr"rrrrVr|rr!rrrr r*)rFrrr0rHrJr/rKr3s     zTimedeltaArray.__rfloordiv____mod__cCs$t||jr t|}||||Sr\rr r rrJrJrKr4 zTimedeltaArray.__mod____rmod__cCs$t||jr t|}||||Sr\r5rrJrJrKr7r6zTimedeltaArray.__rmod__ __divmod__cCs0t||jr t|}||}|||}||fSr\r5rFrres1res2rJrJrKr8  zTimedeltaArray.__divmod__ __rdivmod__cCs0t||jr t|}||}|||}||fSr\r5r9rJrJrKr=r<zTimedeltaArray.__rdivmod__cCs0d}|jdur |j }t|j|j |j|dS)Nr)rcrrr{r|)rFrcrJrJrK__neg__s zTimedeltaArray.__neg__cCst||j|jdS)N)rc)rr{rrcr^rJrJrK__pos__szTimedeltaArray.__pos__cCst|t|jSr\)rrabsr{r^rJrJrK__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 ------- ndarray, Index or Series When the calling object is a TimedeltaArray, the return type is ndarray. When the calling object is a TimedeltaIndex, the return type is an Index with a float64 dtype. 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() Index([0.0, 86400.0, 172800.0, 259200.0, 345600.0], dtype='float64') N)r@)rrCrErB)rFppsrJrJrKrls 6zTimedeltaArray.total_secondscCs t|jS)a  Return an ndarray of datetime.timedelta objects. Returns ------- numpy.ndarray Examples -------- >>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit='D') >>> tdelta_idx TimedeltaIndex(['1 days', '2 days', '3 days'], dtype='timedelta64[ns]', freq=None) >>> tdelta_idx.to_pytimedelta() array([datetime.timedelta(days=1), datetime.timedelta(days=2), datetime.timedelta(days=3)], dtype=object) )rr{r^rJrJrKrk s zTimedeltaArray.to_pytimedeltaaCNumber of days for each element. Examples -------- For Series: >>> ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='d')) >>> ser 0 1 days 1 2 days 2 3 days dtype: timedelta64[ns] >>> ser.dt.days 0 1 1 2 2 3 dtype: int64 For TimedeltaIndex: >>> tdelta_idx = pd.to_timedelta(["0 days", "10 days", "20 days"]) >>> tdelta_idx TimedeltaIndex(['0 days', '10 days', '20 days'], dtype='timedelta64[ns]', freq=None) >>> tdelta_idx.days Index([0, 10, 20], dtype='int64')r<aNumber of seconds (>= 0 and less than 1 day) for each element. Examples -------- For Series: >>> ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='S')) >>> ser 0 0 days 00:00:01 1 0 days 00:00:02 2 0 days 00:00:03 dtype: timedelta64[ns] >>> ser.dt.seconds 0 1 1 2 2 3 dtype: int32 For TimedeltaIndex: >>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit='S') >>> tdelta_idx TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:02', '0 days 00:00:03'], dtype='timedelta64[ns]', freq=None) >>> tdelta_idx.seconds Index([1, 2, 3], dtype='int32')reaNumber of microseconds (>= 0 and less than 1 second) for each element. Examples -------- For Series: >>> ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='U')) >>> ser 0 0 days 00:00:00.000001 1 0 days 00:00:00.000002 2 0 days 00:00:00.000003 dtype: timedelta64[ns] >>> ser.dt.microseconds 0 1 1 2 2 3 dtype: int32 For TimedeltaIndex: >>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit='U') >>> tdelta_idx TimedeltaIndex(['0 days 00:00:00.000001', '0 days 00:00:00.000002', '0 days 00:00:00.000003'], dtype='timedelta64[ns]', freq=None) >>> tdelta_idx.microseconds Index([1, 2, 3], dtype='int32')rfaNumber of nanoseconds (>= 0 and less than 1 microsecond) for each element. Examples -------- For Series: >>> ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='N')) >>> ser 0 0 days 00:00:00.000000001 1 0 days 00:00:00.000000002 2 0 days 00:00:00.000000003 dtype: timedelta64[ns] >>> ser.dt.nanoseconds 0 1 1 2 2 3 dtype: int32 For TimedeltaIndex: >>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit='N') >>> tdelta_idx TimedeltaIndex(['0 days 00:00:00.000000001', '0 days 00:00:00.000000002', '0 days 00:00:00.000000003'], dtype='timedelta64[ns]', freq=None) >>> tdelta_idx.nanoseconds Index([1, 2, 3], dtype='int32')rgr5cs`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 Examples -------- >>> tdelta_idx = pd.to_timedelta(['1 day 3 min 2 us 42 ns']) >>> tdelta_idx TimedeltaIndex(['1 days 00:03:00.000002042'], dtype='timedelta64[ns]', freq=None) >>> tdelta_idx.components days hours minutes seconds milliseconds microseconds nanoseconds 0 1 0 3 0 0 2 42 rr4)r<hoursminutesre millisecondsrfrgcst|r tjgtS|jSr\)r&rrr componentsrWcolumnsrJrKrLsz$TimedeltaArray.components..fcSs|jSr\)rGrWrJrJrKrLscsg|]}|qSrJrJr)rLrJrKrsz-TimedeltaArray.components..rHint64)pandasr5rDr)rFr5hasnansrHrJ)rIrLrKrGs   zTimedeltaArray.components)r:r[)rXrrr:rs)r:rz)rGrrcrr|rzr:r2)rrr:r2r\)rirr:r2)r:rr)r:rs)r:r)T)rr)r:r-) rrr|rrrrrrr) rrr|rrrrrrr)r6r7rr)F)rr)rrr:r)r:r2)r:r)r:rR)r:rB)r:r)r:r5)OrN __module__ __qualname__rO_typrrZ_internal_fill_valuerr r _is_recognized_dtype_infer_matchesrPr___array_priority__ra__annotations__rbrdrhrjrqryr|rr_default_dtype classmethodrrrrrrrrrrrrrrrrrrr,r__rmul__rrrrr.r1r3r4r7r8r=r>r?rArlrktextwrapdedentdays_docstringrQr<seconds_docstringremicroseconds_docstringrfnanoseconds_docstringrgrG __classcell__rJrJrrKrRks %         . 0     !)             9 rRFraiserrerrorsr/r:tuple[np.ndarray, Tick | None]cCs,|dvsJd}|durt|}tj||dd\}}t|tr"|j}|jtks,t|jr6t |||d}d}nt |jrIt ||d\}}|oG| }nt |jrt|jt r[|j}|j}nt|}t|pdd\}}ttd d t|tj} Wdn1swY|| } |rt| |} ttd d t| || |tjd }Wdn1swYt||<d}n4t|jd rt|j} t| st | } t!| } td | d}t"||dd}d}n t#d|jdtj$||d}|jj%d ks J|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. rNrR)cls_namerir`FrrTignorez!invalid value encountered in casttimedelta64[ns]rUrrrdtype ' cannot be converted to timedelta64[ns]rm8)&rr!ensure_arraylike_for_datetimelikerrRrcr|r2r#_objects_to_td64nsr _ints_to_td64nsrr%_mask_datarisnanrwarningscatch_warningsfilterwarningsRuntimeWarningrrJrmrurrrVrrr rr rr kind)rrrir`r copy_maderrUpbasefrac data_unitnew_resonew_unit new_dtyperJrJrKrsh $           rrTricCsxd}|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 FNrTTz timedelta64[rrre)r|rrJrrur r)rrirt dtype_strrJrJrKrk_s      rkcCs*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. Frrcre)rr object_rru)rrir`rGrHrJrJrKrjs rjr0cCsXt|}|tdkrd}t|t|tjr"|jdks"tt|s*td|d|S)NrhzhPassing in 'timedelta' dtype with no precision is not allowed. Please pass in 'timedelta64[ns]' instead.rUrfrg)r$rr|rrrsrr)r|msgrJrJrKr}s   r})r6r7r8r7r9r7)FNr_)rrr`r/r:ra)rT)rir7)Nr_)r`r/)r:r0)Z __future__rdatetimerrtypingrrronumpyr pandas._libsrrpandas._libs.tslibsrr r r r r rrrrrrpandas._libs.tslibs.conversionrpandas._libs.tslibs.fieldsrrpandas._libs.tslibs.timedeltasrrrrrpandas.compat.numpyrrpandas.util._validatorsrpandas.core.dtypes.commonrrr r!r"r#r$pandas.core.dtypes.dtypesr%pandas.core.dtypes.missingr& pandas.corer'r(pandas.core.array_algosr)pandas.core.arraysr*rpandas.core.arrays._rangesr+pandas.core.commoncorecommonrpandas.core.ops.commonr,collections.abcr-pandas._typingr.r/r0r1r2r3rKr5rXrQ TimelikeOpsrRrrkrjr}rJrJrJrKsV 8   $          m ($