+ ŜiRt^RIHt^RIHtHt^RIt^RIH t ^RI H t ^RI Ht^RIHtHt^RIHtHtHtHt^R IHt^R IHt^R IHtHt^RIHuH t!^R I"H#t#^R I$H%t%^RI&H't'^RI(H)t)H*t*H+t+^RI,H-t-^RI.H/t/H0t0]'d^RI1H2t2H3t3^RI4H5t5H6t6H7t7^RI8H9t9]!R4!RR44t:]!RR44t;R RRlltR#)!z] Provide user facing operators for doing the split part of the split-apply-combine paradigm. ) annotations) TYPE_CHECKINGfinalN)algos)OutOfBoundsDatetime)InvalidIndexError)cache_readonly set_module) ensure_int64ensure_platform_int is_list_like is_scalar)CategoricalDtype) algorithms) CategoricalExtensionArray) DataFrame)ops)recode_for_groupby)Index MultiIndex default_index)Series) PrettyDict pprint_thing)HashableIterator) ArrayLikeNDFrameTnpt)NDFramepandasca]tRt^Et$RtR]R&R]R&R]R&RtR]R&V3R ltRR R lltRR Rllt RRR /RRlllt ] RRl4t Rt V;t#)Groupera A Grouper allows the user to specify a groupby instruction for an object. This specification will select a column via the key parameter, or if the level parameter is given, a level of the index of the target object. If ``level`` is passed as a keyword to both `Grouper` and `groupby`, the values passed to `Grouper` take precedence. Parameters ---------- *args Currently unused, reserved for future use. **kwargs Dictionary of the keyword arguments to pass to Grouper. Attributes ---------- key : str, defaults to None Groupby key, which selects the grouping column of the target. level : name/number, defaults to None The level for the target index. freq : str / frequency object, defaults to None This will groupby the specified frequency if the target selection (via key or level) is a datetime-like object. For full specification of available frequencies, please see :ref:`here`. sort : bool, default to False Whether to sort the resulting labels. closed : {'left' or 'right'} Closed end of interval. Only when `freq` parameter is passed. label : {'left' or 'right'} Interval boundary to use for labeling. Only when `freq` parameter is passed. convention : {'start', 'end', 'e', 's'} If grouper is PeriodIndex and `freq` parameter is passed. origin : Timestamp or str, default 'start_day' The timestamp on which to adjust the grouping. The timezone of origin must match the timezone of the index. If string, must be one of the following: - 'epoch': `origin` is 1970-01-01 - 'start': `origin` is the first value of the timeseries - 'start_day': `origin` is the first day at midnight of the timeseries - 'end': `origin` is the last value of the timeseries - 'end_day': `origin` is the ceiling midnight of the last day offset : Timedelta or str, default is None An offset timedelta added to the origin. dropna : bool, default True If True, and if group keys contain NA values, NA values together with row/column will be dropped. If False, NA values will also be treated as the key in groups. Returns ------- Grouper or pandas.api.typing.TimeGrouper A TimeGrouper is returned if ``freq`` is not ``None``. Otherwise, a Grouper is returned. See Also -------- Series.groupby : Apply a function groupby to a Series. DataFrame.groupby : Apply a function groupby. Examples -------- ``df.groupby(pd.Grouper(key="Animal"))`` is equivalent to ``df.groupby('Animal')`` >>> df = pd.DataFrame( ... { ... "Animal": ["Falcon", "Parrot", "Falcon", "Falcon", "Parrot"], ... "Speed": [100, 5, 200, 300, 15], ... } ... ) >>> df Animal Speed 0 Falcon 100 1 Parrot 5 2 Falcon 200 3 Falcon 300 4 Parrot 15 >>> df.groupby(pd.Grouper(key="Animal")).mean() Speed Animal Falcon 200.0 Parrot 10.0 Specify a resample operation on the column 'Publish date' >>> df = pd.DataFrame( ... { ... "Publish date": [ ... pd.Timestamp("2000-01-02"), ... pd.Timestamp("2000-01-02"), ... pd.Timestamp("2000-01-09"), ... pd.Timestamp("2000-01-16"), ... ], ... "ID": [0, 1, 2, 3], ... "Price": [10, 20, 30, 40], ... } ... ) >>> df Publish date ID Price 0 2000-01-02 0 10 1 2000-01-02 1 20 2 2000-01-09 2 30 3 2000-01-16 3 40 >>> df.groupby(pd.Grouper(key="Publish date", freq="1W")).mean() ID Price Publish date 2000-01-02 0.5 15.0 2000-01-09 2.0 30.0 2000-01-16 3.0 40.0 If you want to adjust the start of the bins based on a fixed timestamp: >>> start, end = "2000-10-01 23:30:00", "2000-10-02 00:30:00" >>> rng = pd.date_range(start, end, freq="7min") >>> ts = pd.Series(np.arange(len(rng)) * 3, index=rng) >>> ts 2000-10-01 23:30:00 0 2000-10-01 23:37:00 3 2000-10-01 23:44:00 6 2000-10-01 23:51:00 9 2000-10-01 23:58:00 12 2000-10-02 00:05:00 15 2000-10-02 00:12:00 18 2000-10-02 00:19:00 21 2000-10-02 00:26:00 24 Freq: 7min, dtype: int64 >>> ts.groupby(pd.Grouper(freq="17min")).sum() 2000-10-01 23:14:00 0 2000-10-01 23:31:00 9 2000-10-01 23:48:00 21 2000-10-02 00:05:00 54 2000-10-02 00:22:00 24 Freq: 17min, dtype: int64 >>> ts.groupby(pd.Grouper(freq="17min", origin="epoch")).sum() 2000-10-01 23:18:00 0 2000-10-01 23:35:00 18 2000-10-01 23:52:00 27 2000-10-02 00:09:00 39 2000-10-02 00:26:00 24 Freq: 17min, dtype: int64 >>> ts.groupby(pd.Grouper(freq="17min", origin="2000-01-01")).sum() 2000-10-01 23:24:00 3 2000-10-01 23:41:00 15 2000-10-01 23:58:00 45 2000-10-02 00:15:00 45 Freq: 17min, dtype: int64 If you want to adjust the start of the bins with an `offset` Timedelta, the two following lines are equivalent: >>> ts.groupby(pd.Grouper(freq="17min", origin="start")).sum() 2000-10-01 23:30:00 9 2000-10-01 23:47:00 21 2000-10-02 00:04:00 54 2000-10-02 00:21:00 24 Freq: 17min, dtype: int64 >>> ts.groupby(pd.Grouper(freq="17min", offset="23h30min")).sum() 2000-10-01 23:30:00 9 2000-10-01 23:47:00 21 2000-10-02 00:04:00 54 2000-10-02 00:21:00 24 Freq: 17min, dtype: int64 To replace the use of the deprecated `base` argument, you can now use `offset`, in this example it is equivalent to have `base=2`: >>> ts.groupby(pd.Grouper(freq="17min", offset="2min")).sum() 2000-10-01 23:16:00 0 2000-10-01 23:33:00 9 2000-10-01 23:50:00 36 2000-10-02 00:07:00 39 2000-10-02 00:24:00 24 Freq: 17min, dtype: int64 boolsortdropna Index | None_grouperztuple[str, ...] _attributesc X<VPR4e ^RIHpTp\SV`V4#)freq) TimeGrouper)getpandas.core.resampler,super__new__)clsargskwargsr, __class__s&*, d/Users/mibo/.openclaw/workspace/.venv-ak/lib/python3.14/site-packages/pandas/core/groupby/grouper.pyr0Grouper.__new__s) ::f  ) 8Cws##Nc$V^8dQhRRRRRR/#)r%r$r&returnNone)formats"r5 __annotate__Grouper.__annotate__s*::  :  : :r7c zWnW nW0nW@nWPnRVnRVnRVnRVnR#N) keylevelr+r%r&_indexer_deprecatedbinnerr(_indexer)selfrBrCr+r%r&s&&&&&&r5__init__Grouper.__init__s:    @D   59 r7c(V^8dQhRRRRRRRR/#)r9objrvalidater$observedr:z tuple[ops.BaseGrouper, NDFrameT]r<)r=s"r5r>r?"s,'+>B )r7c  VPV4wpp\VVP.VPVPVVP VR7wrTpWQ3#)aY Parameters ---------- obj : Series or DataFrame Object being grouped. validate : bool, default True If True, validate the grouper. observed : bool, default True Whether only observed groups should be in the result. Only has an impact when grouping on categorical data. Returns ------- A tuple of grouper, obj (possibly sorted) )rCr%rLr&rM) _set_grouper get_grouperrBrCr%r&)rGrKrLrM_groupers&&&& r5 _get_grouperGrouper._get_grouper"sY$%%c* Q%  XXJ**;; C|r7 gpr_indexc(V^8dQhRRRRRRRR/#) r9rKrr%r$rUr'r:z3tuple[NDFrameT, Index, npt.NDArray[np.intp] | None]r<)r=s"r5r>r?As2M M M #'M ?KM <M r7c VfQhVPeVPe \R4hVPfW0nVPVnVPeVPp\ VRR4V8Xd\V\4'dVPfQhVP eRVP P4pVPPV4pVPVP4pMVPPVP4pMWAP9d\RV R24h\W,VR7pMVPpVPeVPp\V\4'd@VP!V4p\VP#V4VP$V,R7pM"V^VP&39d\RV R24hRp VP('g V'dZVP*'gHVP,PR R R 7;qnVPV 4pVPV ^R 7pWV 3#) aG given an object and the specifications, setup the internal grouper for this particular specification Parameters ---------- obj : Series or DataFrame sort : bool, default False whether the resulting grouper should be sorted gpr_index : Index or None, default None Returns ------- NDFrame Index np.ndarray[np.intp] | None Nz2The Grouper cannot specify both a key and a level!namezThe grouper name z is not foundrXz The level z is not valid mergesortfirst)kind na_positionaxis)rBrC ValueErrorr(rDrFgetattr isinstancerargsorttakeindex _info_axisKeyErrorrr_get_level_number_get_level_valuesnamesrXr%is_monotonic_increasingarray) rGrKr%rUrBreverse_indexer unsorted_axaxrCindexers &&&$ r5rOGrouper._set_grouperAs ( 88 DJJ$:QR R == %M 44DM 88 ((Cy&$/36:c6;R;R }}000==,&*mm&;&;&=O"&--"4"4_"EK$))#))4B++CII6Bnn,"%6se=#IJJ38#.Bzz% b*--007Er33E:%QB1bgg,.$z% %FGG04 IIIr'A'A'A241A1A g2B2 G.!B((7(+Cr7cV^8dQhRR/#r9r:strr<)r=s"r5r>r?s&&#&r7c aV3RlSP4pRPV4p\S4PpV RV R2#)c3h<"TF'p\SV4fKV R\SV4: 2xK) R#5i)N=)ra).0 attr_namerGs& r5 #Grouper.__repr__..s7 - tY' 8yk7436 7-s22z, ())r)jointype__name__)rG attrs_listattrscls_namesf r5__repr__Grouper.__repr__sJ !--  *%:&&1UG1%%r7) r(rFrDrEr&r+rBrCr%)rBrCr+r%r&)NNNFT)TT)F)r __module__ __qualname____firstlineno____doc____annotations__r)r0rHrSrOrr__static_attributes__ __classcell__)r4s@r5r#r#Es_yv J L#MKM$:&>M NRM M ^ & &r7r#cX]tRtRt$RtRtR]R&R]R&R]R &R'R R lltR R ltRRlt ] RRl4t ] RRl4t ] RRl4t ]RRl4t] RRl4t]RRl4t]RRl4t] RRl4t] R R!l4t]R"R#l4t] R$R%l4tR&tR#)(Groupingia Holds the grouping information for a single key Parameters ---------- index : Index grouper : obj : DataFrame or Series name : Label level : observed : bool, default False If we are a Categorical, use the observed values in_axis : if the Grouping is a column in self.obj and hence among Groupby.exclusions list dropna : bool, default True Whether to drop NA groups. uniques : Array-like, optional When specified, will be used for unique values. Enables including empty groups in the result for a BinGrouper. Must not contain duplicates. Attributes ------- indices : dict Mapping of {group -> index_list} codes : ndarray Group codes group_index : Index or None unique groups groups : dict Mapping of {group -> label_list} Nz$npt.NDArray[np.signedinteger] | None_codesr' _orig_catsr_indexc8V^8dQhRRRRRRRRRRR RR R R R /#)r9rerrKzNDFrame | Noner%r$rMin_axisr&uniqueszArrayLike | Noner:r;r<)r=s"r5r>Grouping.__annotate__sdg/g/g/ g/  g/g/g/g/"g/ g/r7c  \V\4'dVPRR7pW@nW n\ W4p RVnWnWPnW0n W`n Wpn Wn Wn VPp V eH\V\4'dVP!V 4p MTp V fT p EMT p V P#V 4p EMj\V \$4'dVPfQhV P'VPRR7wrWn \V\(P*4'dTp MVP,^,P.p\1VVP2P4RR7p M\V \\0\6\8P:34'g\=V R^4^8wd$\?\AV 44p\CRV R24hVP#V 4p \EV R4'd\GV 4\GV48Xg\IV 4pR V 2p\KV4h\V \8P:4'd6V PLPNR 9d\V 4PQ4p M>\\=V R R4\R4'dV PTVn\WWV4p WnR#) F)deepN)rLrXcopyndim Grouper for '' not 1-dimensional__len__z9Grouper result violates len(labels) == len(data) result: mMdtype),rbrrrC _orig_grouper_convert_grouperrr_sortrK _observedr_dropna_uniques_ilevelrget_level_valuesmapr#rSr BinGrouper groupingsgrouping_vectorr result_indexrXrnpndarrayrartrr`hasattrlenrAssertionErrorrr\to_numpyr categoriesr)rGrerRrKrCr%rMrr&rrilevel index_levelmapper newgroupernewobjngtgrpererrmsgs&&&&&&&&&& r5rHGrouping.__init__sb gv & &lll.G $*5:  !     %,,#44V< # &"-("-//&"9 1 1 88' ''!0!=!=dhhQV!=!W JH*cnn55#-  ))!,<<"'Z4499# fe^RZZH  2a7_-. =3F!GHH#ii8O33(CJ6$_5PQVPWX%V,, orzz 2 2$$))T1 #)"9"B"B"D $?AQ R R-88DO0QO.r7cV^8dQhRR/#rsr<)r=s"r5r>r+s((#(r7c "RVP R2#)z Grouping(r}rYrGs&r5rGrouping.__repr__+s499+Q''r7cV^8dQhRR/#)r9r:rr<)r=s"r5r>r.s""("r7c ,\VP4#rA)iterindicesrs&r5__iter__Grouping.__iter__.sDLL!!r7cV^8dQhRR/#r9r:r$r<)r=s"r5r>r2s33T3r7c P\VPRR4p\V\4#)rN)rarrbr)rGrs& r5_passed_categoricalGrouping._passed_categorical1s$,,gt<%!122r7cV^8dQhRR/#)r9r:rr<)r=s"r5r>r7shr7c VPpVeVPPV,#\VP\ \ 34'dVPP#\VP\P4'd!VPPP#\VP\ 4'dVPP#R#rA) rrrjrbrrrrXrr BaseGrouperr)rGrs& r5rX Grouping.name6s  ;;$$V, , d((5&/ : :%%** * ,,coo > >''4499 9 ,,e 4 4'',, ,r7cV^8dQhRR/#)r9r:z int | Noner<)r=s"r5r>rIs   r7c VPpVfR#\V\4'gGVPpWP9d\ RV R24hVPP V4#V#)zC If necessary, converted index level name to index level position. NzLevel z not in index)rCrbintrrjrre)rGrCres& r5rGrouping._ilevelHsc   =%%%KKEKK'$veWM%BCC;;$$U+ + r7cV^8dQhRR/#)r9r:rr<)r=s"r5r>rXs!!!r7c ,\VP4#rA)rrrs&r5ngroupsGrouping.ngroupsWs4<<  r7cV^8dQhRR/#)r9r:z$dict[Hashable, npt.NDArray[np.intp]]r<)r=s"r5r>r\s))=)r7c \VP\P4'dVPP#\ VP4pVP 4#rA)rbrrrrr_reverse_indexer)rGvaluess& r5rGrouping.indices[sL d**COO < <''// /T112&&((r7cV^8dQhRR/#)r9r:znpt.NDArray[np.signedinteger]r<)r=s"r5r>res**4*r7c (VP^,#)_codes_and_uniquesrs&r5codesGrouping.codesd&&q))r7cV^8dQhRR/#)r9r:rr<)r=s"r5r>ris***r7c (VP^,#)rrs&r5rGrouping.uniqueshrr7cV^8dQhRR/#)r9r:z/tuple[npt.NDArray[np.signedinteger], ArrayLike]r<)r=s"r5r>rms>>$S>r7c VP'EdVPpVPpVP'dU\P !VP 4pW3R8g,pVP'd\P!V4pM\P!\V44pRpVP'gVP4p\P!V4'dkRpVP'd \V4pM3VP4p\P !VP RV4p\P"!W6R4p\$P&!W2VP(RR7pVP p V'dMVP'g#\P*!V X8V ^,V 4p \P*!XXV 4p W3#\-VP\.P04'd:VPP2p VPP4P6pW3#VP8e=\%VPVP8R7pVP p VP8pW3#\P:!VPVPVPR7wrW3#)rFTN)rrorderedrL)r)r%use_na_sentinel)rrrrrunique1drrrr%arangerrisnaanyargmax nunique_intsinsertr from_codesrwhererbrr codes_infor_valuesr factorize) rGcatrucodeshas_dropped_nana_maskna_codena_idxrrs & r5rGrouping._codes_and_uniquesls  # # #&&CJ~~~#,,SYY7" -:::WWV_F3z?3"N<<<((*66'??%)Nzzz"%j/")!1","9"9#))GV:L"MYYv;F!,,S[[SXGIIEzzzHHUg%5uqy%HE'59> ! ,,coo > >((33E**77??G~]] &d22t}}MCIIEmmG~(11$$4::t||NE~r7cV^8dQhRR/#)r9r:zdict[Hashable, Index]r<)r=s"r5r>rs " "- "r7c aVPwr\P!W PRR7p\P !\ V4\V44wop\V4P4pV3Rl\W3R,RR74p\W$RR7UUu/uF wrVWPPPV4bK" ppp\V4#uuppi)Frc30<"TF wrSWxK R#5irAr<)rxstartendrs& r5rz"Grouping.groups..sX2WJE1U<2Ws:rNNstrictT)rr _with_inferrXlibalgosgroupsort_indexerr rr cumsumziprrdr) rGrrcounts_resultkvresultrs & @r5groupsGrouping.groupss00##G))%H../B5/I3w<X 6f%,,.X#fRjQV2WX58RV5WX5WTQ![[%%a((5WX&!!Ys%&CcV^8dQhRR/#r9r:rr<)r=s"r5r>rs''8'r7c BVP'dV#VP#rA)r_observed_groupingrs&r5observed_groupingGrouping.observed_groupings >>>K&&&r7cV^8dQhRR/#rr<)r=s"r5r>rs  H r7c  \VPVPVPVPVP RVP VPVPR7 pV#)T)rKrCr%rMrr&r) rrrrKrCrrrr)rGgroupings& r5rGrouping._observed_groupingsP KK   **LL<<MM  r7) rrrrrrrrrrCrK)NNNTFFTN)rrrrrrrrHrrrrrXrpropertyrrrrrrrrrr<r7r5rrs@48F 07 Mg/R("33"  !!))****>>@ " "''   r7rc 0V^8dQhRRRRRRRRRRRR /#) r9rKrr%r$rMrLr&r:z5tuple[ops.BaseGrouper, frozenset[Hashable], NDFrameT]r<)r=s"r5r>r>sNV/V/ V/  V/  V/  V/ V/;V/r7c  aSPpVEe\V\4'dT\V4'd\ V4^8Xd V^,pVf%\ V4'dVP V4pRpM\V4'd:\ V4pV^8Xd V^,pMV^8Xd \R4h\R4h\V\4'd,SPPV8wd\RV R24hMV^8gVR8d \R4hRpTp\V\4'dLVPSRVR7wp oVPfV \4S3#V \VP04S3#\V\P4'dV\4S3#\V\ 4'gV.p Rp MTp \ V 4\ V48Hp \";QJdR V 4F 'gK R M RM !R V 44p \";QJdR V 4F 'gK R M RM !R V 44p \";QJdR V 4F 'gK R M RM !R V 44pV 'gV'gV 'gV 'dVf\S\$4'd<\&;QJdV3R lV 4F 'dK RM R M!V3R lV 44pMR\S\(4'gQh\&;QJdV3RlV 4F 'dK RM R M!V3RlV 44pV'g\*P,!V 4.p \V\.\ 34'dVfR.\ V4,p TpMV.\ V 4,p.p\14pRV3RllpRV3Rllp\3V VR R7EF^wppV!V4'dR pVP5VP4MV!V4'dSP6^8wdbVS9d[V'dSP9V^R7R TSV,pppVP6^8wd\RV R24hVP5V4MoSP;V^R7'dRTRpppMO\=V4h\V\4'd-VPeVP5VP4R pMRp\V\>4'g\?VVSVVVVVR7MTpVPAV4EKa \ V4^8Xd\ S4'd \R4h\ V4^8XdIVPA\?\C^4\DPF!.\DPHR744\P!VVW6R7p V \V4S3#)a Create and return a BaseGrouper, which is an internal mapping of how to create the grouper indexers. This may be composed of multiple Grouping objects, indicating multiple groupers Groupers are ultimately index mappings. They can originate as: index mappings, keys to columns, functions, or Groupers Groupers enable local references to level,sort, while the passed in level, and sort are 'global'. This routine tries to figure out what the passing in references are and then creates a Grouping for each one, combined into a BaseGrouper. If observed & we have a categorical grouper, only show the observed values. If validate, then check for key/level overlaps. NzNo group keys passed!z*multiple levels only valid with MultiIndexz level name z is not the name of the indexz2level > 0 or level < -1 only valid with MultiIndexF)rLrMc3h"TF(p\V4;'g\V\4xK* R#5irA)callablerbdictrxgs& r5rzget_grouper..2s&H4ax{99jD&994s22Tc3N"TFp\V\\34xK R#5irA)rbr#rr's& r5rzr)3sH4az!gx%8994s#%c 3"TF4p\V\\\\\ P 34xK6 R#5irA)rblisttuplerrrrr's& r5rzr)4s,IMA 1tUFE2::>??s<>c3<"TF4qSP9;'gVSPP9xK6 R#5irA)columnsrerjrxr(rKs& r5rzr)As3'BFQS[[ 88A$88$s?"?c3T<"TFqSPP9xK R#5irA)rerjr0s& r5rzr)Fs&JTCIIOO';Ts%(cV^8dQhRR/#rr<)r=s"r5r>!get_grouper..__annotate__Vs  4 r7c<\V4'g;SP^8XdR#SPR,pVPV4R#R# \\ \ 3dR#i;i)rFTr)_is_label_likeraxesget_locrg TypeErrorr)rBitemsrKs& r5 is_in_axisget_grouper..is_in_axisVs]c""xx1}HHRLE  c"  i):;  sAA)(A)cV^8dQhRR/#rr<)r=s"r5r>r3fs  $ r7c@<\VR4'gR#SVP,p\ T\4'd=\ T\4'd'TPPTP^4#R# \\\\ 3dR#i;i)rXF) rrXrg IndexErrorrrrbr_mgrreferences_same_values)gprobj_gpr_columnrKs& r5 is_in_objget_grouper..is_in_objfssF##  ]N c6 " "z.&'I'I8822>3F3FJ J *&79LM  sA>>BBr r^rr)rKrCr%rMrr&)r)r%r&r)%rerbrr rr rr`rtrXr#rSrB frozensetrrr,rrallrcomasarray_tuplesafer-setraddr_check_label_or_level_ambiguity_is_level_referencergrappendrrrlintp)rKrBrCr%rMrLr& group_axisnlevelsrRkeysmatch_axis_length any_callable any_groupers any_arraylikeall_in_columns_indexlevelsr exclusionsr:rCrArrXpingsf&&&&&& r5rPrPs>J  j* - -E""s5zQa{y// 11%8 E""e*a<!!HE\$%<==$%QRR%%%99>>U*${5'9V%WXX+ebj !UVVEC#w''eh'O  77?IK, ,Iswwi0#5 5 C ) )IK$$ c4 u!IZ83H4H333H4HHL3H4H333H4HHLCIMCCCIMM   M c9 % %#&3'BF'333'BF'$ c6** **#&3&JT&J333&JT&J#J #))$/0D%%'' ;6CJ&D3t9$ "I #J    $t44 U S>>G NN388 $ __xx1}77!7D%)3Cs88q=%}TF:M%NOOt$((1(55&+S$sm# W % %#''*= NN377 #GGc8,, !   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