a Of@sddlmZddlmZddlZddlmZmZddlZ ddl m Z m Z ddl mZmZmZmZmZmZmZmZmZddlmZddlmZmZdd lmZmZmZm Z m!Z!dd l"m#Z$dd l%m&Z&dd l'm(Z(m)Z)m*Z*m+Z+m,Z,m-Z-m.Z.dd l/m0Z0ddl1m2Z2ddl3m4Z4m5Z5ddl6m7Z7ddl8m9Z:ddl;mm?Z@ddlAmBZBerddlCmDZDddlEmFZFmGZGmHZHmIZImJZJmKZKddlLmMZMddlNZNddddddZOGddde:jPZQd0dd d!d"d#d$ZRd1dd&d'd(ZSd2d d)d*d+ZTd,d-d.d/ZUdS)3) 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 DataFramestr)namealias docstringcs.ddfdd }||_d|d|_t|S)Nz np.ndarrayreturncsH|j}dkrt||jd}nt||jd}|jrD|j|ddd}|S)NdaysZresofloat64) fill_valueconvert)asi8r_cresor_hasna_maybe_mask_results)selfvaluesresultr5V/var/www/ai-form-bot/venv/lib/python3.9/site-packages/pandas/core/arrays/timedeltas.pyfRsz_field_accessor..f )__name____doc__property)r4r5r6rHrFrErG_field_accessorQsrMcseZdZUdZdZeddZeeje fZ ddZ dZ e dd d d Zd ZgZd ed<gZd ed<dgZd ed<gdZd ed<eeedgZd ed<gdZd ed<dddddZe dd ddZd ZeZed!d"Zed efd#d$dd%d&fd'd( Zed d)d*d+d%d,d-d.Zed d)e j!d d/d+d%d,d0d1Z"edd d2d3d%d4d5d6Z#dd d7d8Z$dd d9d:Z%d;d ddEdFdGdHd+d+dIdJdKdLZ)d d d dMd)d>dNdGdHdId+d+dOdPdQZ*d>dRdSd+dTfdUdVZ+dd+dWdXdYZ,dd dZd[d\d]d^d_Z-d`daZ.e/dbd%d dcddZ0e0Z1dedfZ2dgdhZ3did djdkZ4e/dldmdnZ5e/dodpdqZ6e/drdsdtZ7e/dudvdwZ8e/dxdydzZ9e/d{d|d}Z:e/d~ddZ;e/dddZdd ddZ?dd ddZ@d\d ddZAeBCdZDeEddeDZFeBCdZGeEddeGZHeBCdZIeEddeIZJeBCdZKeEddeKZLe dd ddZMZNS)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] ZtimedeltaarrayrnscCs t|dS)Nm)r is_np_dtypexrFrFrGzTimedeltaArray.)r timedelta64ztype[Timedelta]r7cCstSNr rBrFrFrG _scalar_typeszTimedeltaArray._scalar_typeiz list[str] _other_ops _bool_opsfreq _object_ops)r9seconds microseconds nanoseconds _field_opsunit_datetimelike_ops)to_pytimedelta total_secondsroundfloorceilas_unit_datetimelike_methodsznp.timedelta64Timedelta | NaTType)rSr8cCs(|d}|tjkrtStj||jdS)Ni8r:)viewr_valuer Z_from_value_and_resor?)rBrSyrFrFrG _box_funcs  zTimedeltaArray._box_funcznp.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 )_ndarraydtyperYrFrFrGrsszTimedeltaArray.dtypeNcCs(t|}t|j||jkr$td|S)Nz'Values resolution does not match dtype.)_validate_td64_dtypers ValueError)clsrCrsrFrFrG_validate_dtypes   zTimedeltaArray._validate_dtypeznpt.NDArray[np.timedelta64]z Tick | Noner/)rCr]rsr8cstt|dsJt|rJt|tjs6Jt|||jksDJ|dusZt|t sZJt j ||d}||_ |S)NrP)rCrs) rrQrZ is_unitless isinstancenpZndarraytypersr super _simple_new_freq)rvrCr]rsrD __class__rFrGr|szTimedeltaArray._simple_newFrscopybool)rr8cCsF|r t|}t||dd\}}|dur4t||dd}|j||j|dS)NrrcFrrsr])rtsequence_to_td64nsr r|rs)rvdatarsrr]rFrFrG_from_sequences zTimedeltaArray._from_sequence)rsrr]rccCsb|r t|}|dvsJt|||d\}}|dur@t||dd}|j||j|d}||i|S)zo _from_sequence_not_strict but without responsibility for finding the result's `freq`. 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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<)rr?rAr>)rBZppsrFrFrGrfs6 zTimedeltaArray.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) )rrrrYrFrFrGre szTimedeltaArray.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')r9aNumber 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')r_aNumber 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')r`aNumber 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')rar2cs`ddlm}gd|j}|r,fddndd|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 rr1)r9hoursminutesr_Z millisecondsr`racst|rtjgtS|jSrW)r#ryrr componentsrRcolumnsrFrGrHsz$TimedeltaArray.components..fcSs|jSrW)rrRrFrFrGrHscsg|] }|qSrFrFr)rHrFrGrrUz-TimedeltaArray.components..rint64)pandasr2r@r)rBr2ZhasnansrDrF)rrHrGrs   zTimedeltaArray.components)N)T)F)OrJ __module__ __qualname__rKZ_typryrVZ_internal_fill_valuerr rZ_is_recognized_dtypeZ_infer_matchesrLrZZ__array_priority__r[__annotations__r\r^rbrdrkrqrsr}rZ_default_dtype classmethodrwr|rrZ no_defaultrrrrrrrrrrrrrr)r__rmul__rrrrrrrrrrrrrrrfretextwrapdedentZdays_docstringrMr9Zseconds_docstringr_Zmicroseconds_docstringr`Znanoseconds_docstringrar __classcell__rFrFr~rGrNgs %       0 !)         9 rNFraiserr,ztuple[np.ndarray, Tick | None])rerrorsr8cCs||dvs Jd}|dur t|}tj||dd\}}t|trD|j}|jtksXt|jrlt |||d}d}nt |jrt ||d\}}|o| }nt |jrt|jt r|j}|j}n t|}t||pd}t||<|d }d}nLt|jd r t|js2t|j}t||dd }d}ntd |jd |sDt|}ntj||d}|jjd ksdJ|jdkstJ||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. rNrN)cls_namercrFrrOzm8[ns]rPrzdtype z' cannot be converted to timedelta64[ns]rm8)rrZ!ensure_arraylike_for_datetimelikerxrNr]rsrr _objects_to_td64nsr_ints_to_td64nsrr"Z_mask_dataryisnanrrrnrrQrr r rrrkind)rrrcrr copy_maderZ new_dtyperFrFrGrsH$            rrOrcCsrd}|dur|nd}|jtjkr0|tj}d}|dkr`d|d}||}t|td}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 FNrOTz timedelta64[rrtimedelta64[ns])rsryr rrnr r)rrcrZ dtype_strrFrFrGr>s      r)rcCs(tj|tjd}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. rrr)ryrZobject_rrn)rrcrrCrDrFrFrGrfsrr-r7cCsTt|}|tdkr"d}t|t|ds@td|dnt|sPtd|S)NrzhPassing in 'timedelta' dtype with no precision is not allowed. Please pass in 'timedelta64[ns]' instead.rPzdtype 'z,' is invalid, should be np.timedelta64 dtypez;Supported timedelta64 resolutions are 's', 'ms', 'us', 'ns')r!ryrsrurrQr)rsmsgrFrFrGrts rt)FNr)rO)Nr)V __future__rdatetimerrtypingrrnumpyryZ pandas._libsrrZpandas._libs.tslibsrr r r r r rrrZpandas._libs.tslibs.conversionrZpandas._libs.tslibs.fieldsrrZpandas._libs.tslibs.timedeltasrrrrrZpandas.compat.numpyrrZpandas.util._validatorsrZpandas.core.dtypes.commonrrrrrr r!Zpandas.core.dtypes.dtypesr"Zpandas.core.dtypes.missingr#Z pandas.corer$r%Zpandas.core.array_algosr&Zpandas.core.arraysr'rZpandas.core.arrays._rangesr(Zpandas.core.commoncorecommonrZpandas.core.ops.commonr)collections.abcr*Zpandas._typingr+r,r-r.r/r0r r2rrMZ TimelikeOpsrNrrrrtrFrFrFrGsR  ,   $        `($