+ xȇi!yRt^RIHt^RIHt^RIHt^RIt^RIH t H t H t H t ^RI t ^RIt^RIHt^RIHt^RIHt^R IHt^R IHtHt^R IHt^R IHtHt^R I H!t!^RI"H#t#H$t$^RI%H&uH't(^RI)H*t*H+t+H,t,H-t-H.t.H/t/H0t0H1t1^RI2H3t3^RI4H5t5] 'd^RI6H7t7H8t8H9t9H:t:^RI;Ht>^RI?H@t@HAtA] RRRRRRRRRRRRRRRRRR/ RR ll4tB] RRRRRRRRRRRRRRRRRR/ R!R"ll4tB] RRRRRRRRRRRRRRRRRR/ R#R$ll4tB] RRRRRRRRRRRRRRRR/R%R&ll4tB] RRRRRRRRRRRRRRRRRR/ R'R(ll4tB]!R)4R^RR*RR+RRRRRRRR+R]PR]P/ R,R-ll4tBR.R/ltDR0R1ltER2R3ltFR4R5ltGR6R7ltHR8R9ltIR:R;ltJR<R=ltKR>R?ltLRBR@RAlltMR#)Cz Concat routines. ) annotations)abc)pairwiseN) TYPE_CHECKINGLiteralcastoverload)lib)Pandas4Warning) set_module)find_stack_level)is_bool is_scalar) concat_compat) ABCDataFrame ABCSeries)isna)factorize_from_iterablefactorize_from_iterables)Index MultiIndexall_indexes_same default_index ensure_indexget_objs_combined_axisget_unanimous_names union_indexes) DatetimeIndex)concatenate_managers)CallableHashableIterableMapping)AxisAxisInt HashableT) DataFrameSeriesaxis.join ignore_indexkeyslevelsnamesverify_integritysortcopyc@V^8dQhRRRRRRRRR R R R R RRRRRRR/ #)objsz3Iterable[DataFrame] | Mapping[HashableT, DataFrame]r(Literal[0, 'index']r)strr*boolr+Iterable[Hashable] | Noner-list[HashableT] | Noner.r/r0bool | lib.NoDefaultreturnr&)formats"j/Users/max/.openclaw/workspace/postharvest/venv/lib/python3.14/site-packages/pandas/core/reshape/concat.py __annotate__r>Nsn   =        $  "       c R#Nr; r3r(r)r*r+r,r-r.r/r0s &$$$$$$$$$r=concatrCMr?c@V^8dQhRRRRRRRRR R R R R RRRRRRR/ #)r2r3z-Iterable[Series] | Mapping[HashableT, Series]r(r4r)r5r*r6r+r7r-r8r.r/r0r9r:r'r;)r<s"r=r>r>^sn   7        $  "        r?c R#rAr;rBs &$$$$$$$$$r=rCrC]sr?c@V^8dQhRRRRRRRRR R R R R RRRRRRR/ #)r2r3EIterable[Series | DataFrame] | Mapping[HashableT, Series | DataFrame]r(r4r)r5r*r6r+r7r-r8r.r/r0r9r:DataFrame | Seriesr;)r<s"r=r>r>nsn   O        $  "       r?c R#rAr;rBs &$$$$$$$$$r=rCrCmr?c@V^8dQhRRRRRRRRR R R R R RRRRRRR/ #)r2r3rHr(zLiteral[1, 'columns']r)r5r*r6r+r7r-r8r.r/r0r9r:r&r;)r<s"r=r>r>~sn   O        $  "       r?c R#rAr;rBs &$$$$$$$$$r=rCrC}rDr?c@V^8dQhRRRRRRRRR R R R R RRRRRRR/ #)r2r3rHr(r#r)r5r*r6r+r7r-r8r.r/r0r9r:rIr;)r<s"r=r>r>sn   O        $  "       r?c R#rAr;rBs &$$$$$$$$$r=rCrCrKr?pandasouterFc@V^8dQhRRRRRRRRR R R R R RRRRRRR/ #)r2r3rHr(r#r)r5r*r6r+r7r-r8r.r/r9r0r:rIr;)r<s"r=r>r>szVV OV V  V  V $ V "VV V VVr?c V'dVe\RV: R24hV \PJd&\P!R\ \ 4R7VR8XdRp MVR8XdR p M \R 4h\W4wrp V\PJEd`V^8XdIVU u.uF;p \V \4'd V PM\V P.4NK= p p MVU u.uFqPNK p p V 'g\;QJdR V 4F 'gK R M RM !R V 44'g\;QJd%R \!V 44F 'dK RM R M!R \!V 444'gf\;QJd%R \!V 44F 'dK RM R M!R \!V 444'dV R,P"'dRpM+\%V4'g\RV R24h\'V4p\)W WFWZ4wrVP*^8Xd^RIHpVP0!V4pRpR pM&VP1V4pR pRpVP3V4p\5V 4^8d\7WVV4pTpV'd ^V, M^pT;'g\9VRR4p\;VVVVV VVVVVV4 pV\PJdV^8XdIVU u.uF;p \V \4'd V PM\V P.4NK= p p MVU u.uFqPNK p p \=V RR7pV^8Xd VPM VPpVP?V4'g(Rp\P!V\ \ 4R7V#uup iuup iuup iuup i)a Concatenate pandas objects along a particular axis. Allows optional set logic along the other axes. Can also add a layer of hierarchical indexing on the concatenation axis, which may be useful if the labels are the same (or overlapping) on the passed axis number. Parameters ---------- objs : an iterable or mapping of Series or DataFrame objects If a mapping is passed, the keys will be used as the `keys` argument, unless it is passed, in which case the values will be selected (see below). Any None objects will be dropped silently unless they are all None in which case a ValueError will be raised. axis : {0/'index', 1/'columns'}, default 0 The axis to concatenate along. join : {'inner', 'outer'}, default 'outer' How to handle indexes on other axis (or axes). ignore_index : bool, default False If True, do not use the index values along the concatenation axis. The resulting axis will be labeled 0, ..., n - 1. This is useful if you are concatenating objects where the concatenation axis does not have meaningful indexing information. Note the index values on the other axes are still respected in the join. keys : sequence, default None If multiple levels passed, should contain tuples. Construct hierarchical index using the passed keys as the outermost level. levels : list of sequences, default None Specific levels (unique values) to use for constructing a MultiIndex. Otherwise they will be inferred from the keys. names : list, default None Names for the levels in the resulting hierarchical index. verify_integrity : bool, default False Check whether the new concatenated axis contains duplicates. This can be very expensive relative to the actual data concatenation. sort : bool, default False Sort non-concatenation axis. One exception to this is when the non-concatenation axis is a DatetimeIndex and join='outer' and the axis is not already aligned. In that case, the non-concatenation axis is always sorted lexicographically. copy : bool, default False This keyword is now ignored; changing its value will have no impact on the method. .. deprecated:: 3.0.0 This keyword is ignored and will be removed in pandas 4.0. Since pandas 3.0, this method always returns a new object using a lazy copy mechanism that defers copies until necessary (Copy-on-Write). See the `user guide on Copy-on-Write `__ for more details. Returns ------- object, type of objs When concatenating all ``Series`` along the index (axis=0), a ``Series`` is returned. When ``objs`` contains at least one ``DataFrame``, a ``DataFrame`` is returned. When concatenating along the columns (axis=1), a ``DataFrame`` is returned. See Also -------- DataFrame.join : Join DataFrames using indexes. DataFrame.merge : Merge DataFrames by indexes or columns. Notes ----- The keys, levels, and names arguments are all optional. A walkthrough of how this method fits in with other tools for combining pandas objects can be found `here `__. It is not recommended to build DataFrames by adding single rows in a for loop. Build a list of rows and make a DataFrame in a single concat. Examples -------- Combine two ``Series``. >>> s1 = pd.Series(["a", "b"]) >>> s2 = pd.Series(["c", "d"]) >>> pd.concat([s1, s2]) 0 a 1 b 0 c 1 d dtype: str Clear the existing index and reset it in the result by setting the ``ignore_index`` option to ``True``. >>> pd.concat([s1, s2], ignore_index=True) 0 a 1 b 2 c 3 d dtype: str Add a hierarchical index at the outermost level of the data with the ``keys`` option. >>> pd.concat([s1, s2], keys=["s1", "s2"]) s1 0 a 1 b s2 0 c 1 d dtype: str Label the index keys you create with the ``names`` option. >>> pd.concat([s1, s2], keys=["s1", "s2"], names=["Series name", "Row ID"]) Series name Row ID s1 0 a 1 b s2 0 c 1 d dtype: str Combine two ``DataFrame`` objects with identical columns. >>> df1 = pd.DataFrame([["a", 1], ["b", 2]], columns=["letter", "number"]) >>> df1 letter number 0 a 1 1 b 2 >>> df2 = pd.DataFrame([["c", 3], ["d", 4]], columns=["letter", "number"]) >>> df2 letter number 0 c 3 1 d 4 >>> pd.concat([df1, df2]) letter number 0 a 1 1 b 2 0 c 3 1 d 4 Combine ``DataFrame`` objects with overlapping columns and return everything. Columns outside the intersection will be filled with ``NaN`` values. >>> df3 = pd.DataFrame( ... [["c", 3, "cat"], ["d", 4, "dog"]], columns=["letter", "number", "animal"] ... ) >>> df3 letter number animal 0 c 3 cat 1 d 4 dog >>> pd.concat([df1, df3], sort=False) letter number animal 0 a 1 NaN 1 b 2 NaN 0 c 3 cat 1 d 4 dog Combine ``DataFrame`` objects with overlapping columns and return only those that are shared by passing ``inner`` to the ``join`` keyword argument. >>> pd.concat([df1, df3], join="inner") letter number 0 a 1 1 b 2 0 c 3 1 d 4 Combine ``DataFrame`` objects horizontally along the x axis by passing in ``axis=1``. >>> df4 = pd.DataFrame( ... [["bird", "polly"], ["monkey", "george"]], columns=["animal", "name"] ... ) >>> pd.concat([df1, df4], axis=1) letter number animal name 0 a 1 bird polly 1 b 2 monkey george Prevent the result from including duplicate index values with the ``verify_integrity`` option. >>> df5 = pd.DataFrame([1], index=["a"]) >>> df5 0 a 1 >>> df6 = pd.DataFrame([2], index=["a"]) >>> df6 0 a 2 >>> pd.concat([df5, df6], verify_integrity=True) Traceback (most recent call last): ... ValueError: Indexes have overlapping values: ['a'] Append a single row to the end of a ``DataFrame`` object. >>> df7 = pd.DataFrame({"a": 1, "b": 2}, index=[0]) >>> df7 a b 0 1 2 >>> new_row = pd.Series({"a": 3, "b": 4}) >>> new_row a 3 b 4 dtype: int64 >>> pd.concat([df7, new_row.to_frame().T], ignore_index=True) a b 0 1 2 1 3 4 NzCannot set ignore_index=z) and specify keys. Either should be used.zThe copy keyword is deprecated and will be removed in a future version. Copy-on-Write is active in pandas since 3.0 which utilizes a lazy copy mechanism that defers copies until necessary. Use .copy() to make an eager copy if necessary.) stacklevelrQFinnerTz?Only can inner (intersect) or outer (union) join the other axisc3L"TFp\V\4'*xK R#5irA) isinstancer).0indexs& r= concat..sU_Ez%777_s"$c3,"TF wrWJxK R#5irAr;rXprevcurrs& r=rZr[sL2KJD4<2Ksc3"TFVwrVP'dKVP'dK-VR,V^,8*;'d VPxKX R#5i)N)emptyis_monotonic_increasingr]s& r=rZr[sN&? ::I.2jjIDHQ'HHD,H,HH&?sA A A  A z0The 'sort' keyword only accepts boolean values; z was passed.)r&r-)r/aSorting by default when concatenating all DatetimeIndex is deprecated. In the future, pandas will respect the default of `sort=False`. Specify `sort=True` or `sort=False` to silence this message. If you see this warnings when not directly calling concat, report a bug to pandas.rb) ValueErrorr no_defaultwarningswarnr r _clean_keys_and_objsrWrcolumnsrnamerYanyallrrdr r6_get_sample_objectndimrPr&_get_axis_number_get_block_manager_axislen_sanitize_mixed_ndimgetattr _get_resultrequals)r3r(r)r*r+r,r-r.r/r0 intersectndimsobjnon_concat_axissampler&bm_axisis_frame is_series orig_axisresultno_sort_result_indexorigmsgs&$$$$$$$$$ r=rCrCsF(',(Q R   3>>!  : ')   w   M  -T8D s~~ 19 C *#|<< % BSS O 599DSyyDO9 sU_UsssU_UUUsL(?2KLsssL(?2KLLL&.&?&.&? $B'???D T]]>tfL Q  Dz&d4RLF{{a$,,T2 ))$/ 009 5zA~#D,HI"1w;D  1 1WT7D1E       F s~~ > C *#|<< % BSS O 599DSyyDO9,_5I(A~v||6>>#**400C  MM#~:J:L M Ms :D :s#AO +OAO Oc ,V^8dQhRRRRRRRRR R/#) r2r3list[Series | DataFrame]r{zSeries | DataFramer*r6r(r$r:r;)r<s"r=r>r>s:// "/ //  /  /r?c.p^pVPpVFpVPpW8XdMW^, 8wd \R4h\VRR4p Rp V'gV flV^8Xd V f^p Rp MV fRp Tp V^, pVPVRR7p\ V\ 4'd!V 'd\ W^,^4VnMVPW/RR7pVPV4K V#)z>cannot concatenate unaligned mixed dimensional NDFrame objectsrkNFT)r0) rorert _constructorrWrrangerjappend) r3r{r*r(new_objscurrent_columnmax_ndimryrorkrename_columnss &&&& r=rsrssHN{{Hxx    \ !P  3-D"Nt|19| )-\%)N)D"a'N))#E):c<00^"'Qh":CK))4+E)BAD Or?c@V^8dQhRRRRRRRRRRR R R R R RRRRR/ #)r2r3rr~r6r|r$r*rwr/r9r+r7r.r-r8r(r;)r<s"r=r>r>)sxo o "o o o  o  o  o  $o o  "o  o r?c RV'Ed\RV^,4p V^8Xd\P!V4p V Pp VUu.uFqPNK pp\ V^R7pV'd\ \V44pM\VVVVVVV 4p\V P4PVVR7pV PVVPR7pV VnVP\ P"!WR7RR7#\%\'V44pV P(p \+VV^,P-^4VVR7p\WW&WxV 4pV !VVRR 7pVVnVP\ P"!WR7RR7#\R V^,4p .p\1VVVVVV V VVV4 pVFp/p\'V4FVwppVV8XdKVP^V, ,pVP3V4'dKBVP5V4VV&KX VP7VPV34K \9VVVRR 7pV PVVPR7p V P\ P"!WR7RR7#uupi) r')r()rYaxes) input_objsr3rC)methodr(rwr/F)rYr0r&) concat_axisr0)rcomconsensus_name_attrr_valuesrrrr_get_concat_axis_seriestype_mgr from_array_constructor_from_mgrr_name __finalize__typesSimpleNamespacedict enumerate_constructor_expanddimrrqrjnew_axesrv get_indexerrr)!r3r~r|r*rwr/r+r,r.r-r(r{rkconsserarrsres new_indexmgrrdatarYrjdf mgrs_indexers result_axesryindexersax new_labels obj_labelsnew_dataouts!&&&&&&&&&&& r=ruru)s"yhQ( a<**40D&&D+/04CKK4D01-C#0S#: 3 $ v{{#..s).DC11#CHH1EFFL&&%%A('   $(D00D*!W44Q7# E .G6UGd%e4B BJ??%%A(#  k47+             CH"+K"8J=!XXa"f- !((44#-#9#9*#EHRL#9  #((H!5 6( ;G% **8(--*H  ! !T =h   i1s J$c@V^8dQhRRRRRRRRR R R R R RRRRRRR/ #)r2r3rr|r$rwr6r/r9r+r7r-r8r(r.r*r: list[Index]r;)r<s"r=r>r>sl "    $  "  r?c \^4U u.uF?p W8Xd\VVV VVVV4M$\VV^,PV 4VVR7NKA up #uup i)z1Return the new [index, column] result for concat.r)r_get_concat_axis_dataframerrq) r3r|rwr/r+r-r(r,r.r*is &&&&&&&&&& r=rrs|<q# "A < #        $ a003   #  sAAc4V^8dQhRRRRRRRRR RR R R R /#)r2r3rr*r6r|r$r+r7r.r-r8r:rr;)r<s"r=r>r>sN-3-3 "-3-3-3 $ -3  -3 "-3 -3r?cV'd\\V44#V^8XdVUu.uFqwPNK ppVfVe \R4h\ V4p M \ WWF4p V'dEV P 'g3WP4,P4p \RV 24hV #VfR.\V4,p ^p Rp \V4FewrVP^8wd#\R\V4P R24hVPeVPW&Rp KXWV&V ^, p Kg V 'd \V 4#\\V44#\!V4P#V4#uupi)zr>sN "  $  "  r?cjaV3RlV4pV'd\\RV444#\V4pVfVe \R4h\ V4p M \ WWT4p V'dEV P 'g3WP4,P4p \RV 24hV #)z?Return result concat axis when concatenating DataFrame objects.c3H<"TFqPS,xK R#5irAr)rXrr(s& r=rZ-_get_concat_axis_dataframe..s.A66$<<s"c38"TFp\V4xK R#5irA)rr)rXrs& r=rZrs =AQsrr) rsumlistrerrrrr) r3r(r*r+r-r,r. indexes_genrrrs &f&&&&& r=rrs/.KS = ==>>{# |  JK K%g. -gVK  5 5 54467>>@r> s"@#@# O@#=@#r?cb\V\P4'd-VfVP4pVUu.uF q V,NK ppMz\V\\ 34'g\ V4'd#\R\V4P R24h\V\P4'g \V4p\V4^8Xd \R4hVe_\V\4'g \V4p\V4\V48wd%\R\V4 R\V4 R24h.p.p\4p\!V4F~wrgVfK \V\\ 34'd@VP#V4VP#V4VP%VP&4KfR\V4 R2p\V4h Ve+\V4\V48dVP)V4p\V4^8Xd \R 4hWAV3#uupi) a- Returns ------- clean_objs : list[Series | DataFrame] List of DataFrame and Series with Nones removed. keys : Index | None None if keys was None Index if objs was a Mapping or keys was not None. 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