!hddlmZddlmZddlZddlmZmZddlZ ddl m Z m Z ddl mZmZmZmZmZmZmZmZmZddlmZddlmZmZdd lmZmZmZm Z m!Z!dd l"m#Z$dd l%m&Z&dd l'm(Z(m)Z)m*Z*m+Z+m,Z,m-Z-m.Z.dd l/m0Z0ddl1m2Z2ddl3m4Z4m5Z5ddl6m7Z7ddl8m9Z:ddl;mcm?Z@ddlAmBZBerddlCmDZDddlEmFZFmGZGmHZHmIZImJZJmKZKddlLmMZMddlNZNd-dZOGdde:jPZQ d.d/d&ZRd0d1d)ZSd2d3d*ZTd4d,ZUdS)5) annotations) timedeltaN) TYPE_CHECKINGcast)libtslibs) NaTNaTTypeTick Timedeltaastype_overflowsafeget_supported_dtypeiNaTis_supported_dtypeperiods_per_second)cast_from_unit_vectorized)get_timedelta_daysget_timedelta_field)array_to_timedelta64floordiv_object_arrayints_to_pytimedeltaparse_timedelta_unittruediv_object_array)function)validate_endpoints) TD64NS_DTYPEis_float_dtypeis_integer_dtypeis_object_dtype is_scalaris_string_dtype pandas_dtype)ExtensionDtype)isna)nanops roperator)datetimelike_accumulations) datetimelike)generate_regular_range)unpack_zerodim_and_defer)Iterator)AxisIntDateTimeErrorChoicesDtypeObjNpDtypeSelfnpt DataFramenamestralias docstringcRdfd }||_d|d|_t|S)Nreturn np.ndarrayc|j}dkrt||j}nt||j}|jr||dd}|S)Ndaysresofloat64) fill_valueconvert)asi8r_cresor_hasna_maybe_mask_results)selfvaluesresultr6s Wc:\xampp_lite_8_4\www\timesheet\venv\Lib\site-packages\pandas/core/arrays/timedeltas.pyfz_field_accessor..fRss F??'T[AAAFF )T[IIIF ; --4.F  )r9r:)__name____doc__property)r4r6r7rJs ` rI_field_accessorrPQsH AJ"Y"""AI A;;rKceZdZUdZdZejddZeeje fZ dZ dZ e dvd Zd ZgZd ed <gZd ed <dgZd ed<gdZd ed<eezezdgzZd ed<gdZd ed<dwdZe dxdZdZeZedZedefdyfd# Zedd$d%dzd(Zedd$e j!dd)dzd*Z"e d{dd+d|d-Z#d}d.Z$d~d/Z%dd1Z&ddd3Z'dd5Z(dddd$dd2d6d7dd?Z)dddd@d$d2dAddCZ*d2dDdfdGZ+dddIZ,dddJddNZ-dOZ.e/dPddQZ0e0Z1dRZ2dSZ3ddUZ4e/dVdWZ5e/dXdYZ6e/dZd[Z7e/d\d]Z8e/d^d_Z9e/d`daZ:e/dbdcZ;e/dddeZddhZ?ddjZ@ddkZAeBjCdlZDeEdmdmeDZFeBjCdnZGeEdodoeGZHeBjCdpZIeEdqdqeIZJeBjCdrZKeEdsdseKZLe dduZMxZNS)TimedeltaArrayal 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._from_sequence(pd.TimedeltaIndex(['1h', '2h'])) ['0 days 01:00:00', '0 days 02:00:00'] Length: 2, dtype: timedelta64[ns] timedeltaarrayr nsc,tj|dS)Nm)r is_np_dtypexs rIzTimedeltaArray.sS_Q%<%<rK)r timedelta64r9type[Timedelta]ctSNr rFs rI _scalar_typezTimedeltaArray._scalar_typesrKiz list[str] _other_ops _bool_opsfreq _object_ops)r<seconds microseconds nanoseconds _field_opsunit_datetimelike_ops)to_pytimedelta total_secondsroundfloorceilas_unit_datetimelike_methodsrYnp.timedelta64Timedelta | NaTTypec|d}|tjkrtStj||jS)Ni8r=)viewr _valuer _from_value_and_resorC)rFrYys rI _box_funczTimedeltaArray._box_funcs9 FF4LL  ??J-adkBBBBrKnp.dtype[np.timedelta64]c|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``. 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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)rFppss rIrmzTimedeltaArray.total_secondss3l!--'' CD'IIIrKc*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`s rIrlzTimedeltaArray.to_pytimedelta s$#4=111rKaCNumber 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')rfaNumber of microseconds (>= 0 and less than 1 second) for each element. Examples -------- For Series: >>> ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='us')) >>> 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='us') >>> 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')rgaNumber of nanoseconds (>= 0 and less than 1 microsecond) for each element. Examples -------- For Series: >>> ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='ns')) >>> 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='ns') >>> 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')rhr3cddlm}gd|j}|rfdnd|fd|D}|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 rr2)r<hoursminutesrf millisecondsrgrhcjt|rtjgtzS|jSr^)r$rrr components)rYcolumnss rIrJz$TimedeltaArray.components..fs.773F8c'll22|#rKc|jSr^)rirXs rIrJz$TimedeltaArray.components..fs |#rKc&g|] }|Srr)rrYrJs rIrz-TimedeltaArray.components..s!///QAAaDD///rK)rjint64)pandasr3rDr)rFr3hasnansrHrjrJs @@rIrizTimedeltaArray.componentss, %$$$$$   +  $ $ $ $ $ $ $ $ $ $////$///AAA ,]]7++F rK)r9r\)rYrsr9rt)r9r|)rGrrdrrr|r9r0)rrr9r0r^)rjrr9r0)r9rs)r9rt)r9r)T)rr)r9r+) rrrrrrrrrr) rrrrrrrrrr)r4r5rr)F)rr)rrr9r)r9r0)r9r$)r9rR)r9r`)r9r)r9r3)OrM __module__ __qualname__rN_typrr[_internal_fill_valuerr r_is_recognized_dtype_infer_matchesrOra__array_priority__rb__annotations__rcrerirkrrr{rrr_default_dtype classmethodrrrr no_defaultrrrrrrrrrrrr r r*r__rmul__r!r#r,r-r?rDrIrLrOrQrVrZr\r_rmrltextwrapdedentdays_docstringrPr<seconds_docstringrfmicroseconds_docstringrgnanoseconds_docstringrhri __classcell__)rs@rIrRrRgs##J D)2>%66$bnd;<<1N XJI$XK%%%%NNNJNNNN#- #;i#G6(#RRRRR(((CCCC ###X#& E!N[!*6 ["+/e B B B B B[ B  ^ [8/3+NKO+N+N+N+N+N[+N`1111        IIIII6%%%%, $ $ 999999. $ $ //////,8< F F F F F F F F66666 (-$ = = = = = =   i((BBB)(BBH'Q'Q'QR,m,,""-,"8n--"".-"(n--"".-"8o..""/.""i((..)(. j))..*). l++,+m,,-,SSSS     OOOO7J7J7J7Jr2222(%X_ )N8 ?66> : :D' '8oG -X_ ':#?L ,HO ':"/K 000X00000rKrRFraiserrerrorsr-r9tuple[np.ndarray, Tick | None]c|dvsJd}|t|}tj||d\}}t|tr|j}|jtkst|jrt|||}d}n"t|jrt||\}}|o| }nt|jrqt|jtr|j}|j}nt!j|}t%||pd}t&||<|d }d}not+j|jd r=t/|js(t1|j}t3||d }d}nt5d |jd |st!j|}nt!j||}|jjd ksJ|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_namerjrFrrTzm8[ns]rVrzdtype z' cannot be converted to timedelta64[ns]rm8)rr!ensure_arraylike_for_datetimelikerrRrdrrHr!_objects_to_td64nsr_ints_to_td64nsrr#_mask_datarisnanrrrwrrWrrr rr&rkind)rrrjrr copy_mader+ new_dtypes rIrrs&H  & & & &M #D))6 d-JD$$''"  zVtz::!$T&AAA $* % %V)$T:::i%I   # #V dj. 1 1 ":D:DD8D>>D(t|t<<T yy"" S ) ) V!$*-- +DJ77I&t95IIIDDTTTTUUU )z$x4((( :?c ! ! ! ! :      rKrTrjc$d}||nd}|jtjkr!|tj}d}|dkr4d|d}||}t |t }d}n|d}||fS) a Convert an ndarray with integer-dtype to timedelta64[ns] dtype, treating the integers as multiples of the given timedelta unit. Parameters ---------- data : numpy.ndarray with integer-dtype unit : str, default "ns" The timedelta unit to treat integers as multiples of. Returns ------- numpy.ndarray : timedelta64[ns] array converted from data bool : whether a copy was made FNrTTz timedelta64[rrtimedelta64[ns])rrrmrrwr r)rrjr dtype_strs rIrr>s I#44D zRX{{28$$  t||*4*** yy##"4|<<< yy*++ ?rKctj|tj}t|||}|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. rrr)rr&object_rrw)rrjrrGrHs rIrrfsA<ZBJ / / /F !&tF C C CF ;;( ) ))rKr.ct|}|tjdkrd}t|t j|dstd|dt |std|S)NrzhPassing in 'timedelta' dtype with no precision is not allowed. Please pass in 'timedelta64[ns]' instead.rVzdtype 'z,' is invalid, should be np.timedelta64 dtypez;Supported timedelta64 resolutions are 's', 'ms', 'us', 'ns')r"rrrrrWr)rmsgs rIrrs   E  8 oo ?5# & &XV5VVVWWW  & &XVWWW LrK)r4r5r6r5r7r5)FNr)rrrr-r9r)rT)rjr5)Nr)rr-)r9r.)V __future__rdatetimerr'typingrrnumpyr pandas._libsrrpandas._libs.tslibsr r r r r rrrrpandas._libs.tslibs.conversionrpandas._libs.tslibs.fieldsrrpandas._libs.tslibs.timedeltasrrrrrpandas.compat.numpyrrpandas.util._validatorsrpandas.core.dtypes.commonrrrrr 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-r.r/r0r1rnr3r|rP TimelikeOpsrRrrrrrrKrIrs""""""                       EDDDDD/.....666666544444++++++?>>>>>222222====== ;;;;;; !((((((!     ,p p p p p S_p p p r #* ]]]]]@%%%%%P!*!*!*!*!*HrK