+ Ŝi ^RIHtRtRt]F t]!]4K AA^RIH t ^R IHtHtHtHtHtHt^RIt^R IHtHtHtHtHtHtHtHtH t H!t!H"t"H#t#H$t$H%t%H&t&H't'H(t(H)t)H*t*H+t+H,t,H-t-H.t.H/t/H0t0H1t1H2t2H3t3H4t4H5t5H6t6H7t7H8t8H9t9H:t:H;t;Ht>H?t?H@t@HAtAHBtBHCtCHDtDHEtEHFtFHGtGHHtHHItIHJtJHKtKHLtLHMtMHNtN^R IOHPtP^R IQHRtR^R ISHTtT^RIUHVtV^RIWHXtX^RIYHZtZH[t[H\t\H]t]H^t^H_t_H`t`HataHbtbHctcHdtdHeteHftfHgtg^RIHhthHitiHjtjHktkHltlHmtm^RIHntn^RIoHptp^RIqHrtrHstsHtttHutuHvtvHwtwHxtxHytyHztzH{t{H|t|H}t}H~t~HtHtHtHtHtHtHtHtHtHtHt^RIHt^RIHtRt^RIHtHtRtRt.RNR NR!NR"NR#NR$NR%NR&NR'NR(NR)NR*NR+NR,NR-NR.NR/NR0NR1NR2NR3NR4NR5NR6NR7NR8NR9NR:NR;NRNR?NR@NRANRBNRCNRDNRENRFNRGNRHNRINRJNRKNRLNRMNRNNRONRPNRQNRRNRSNRTNRUNRVNRWNRXNRYNRZNR[NR\NR]NR^NR_NR`NRaNRbNRcNRdNReNRfNRgNRhNRiNRjNRkNRlNRmNRnNRoNRpNRqNRrNRsNRtNRuNRvNRwNRxNRyNRzNR{NR|NR}NR~NRNRNRNRNRNRNRNRNRNRNRNRNRNRNRNRNRNRNtR# ]dt]!R] R24]hRtAii;i ]dt ] Pt ]!R] R24] hRt A ii;i ]d@^RIHt]!4t]EP+R]R,4t]EP+R4tAAELsi;i)) annotationsrestructuredtextz%Unable to import required dependency z'. Please see the traceback for details.N) is_numpy_devz C extension: z not built. If you want to import pandas from the source directory, you may need to run 'python -m pip install -ve . --no-build-isolation -Ceditable-verbose=true' to build the C extensions first.) get_option set_option reset_optiondescribe_optionoption_contextoptions)7 ArrowDtype Int8Dtype Int16Dtype Int32Dtype Int64Dtype UInt8Dtype UInt16Dtype UInt32Dtype UInt64Dtype Float32Dtype Float64DtypeCategoricalDtype PeriodDtype IntervalDtypeDatetimeTZDtype StringDtype BooleanDtypeNAisnaisnullnotnanotnullIndexCategoricalIndex RangeIndex MultiIndex IntervalIndexTimedeltaIndex DatetimeIndex PeriodIndex IndexSliceNaTPeriod period_range Timedeltatimedelta_range Timestamp date_range bdate_rangeIntervalinterval_range DateOffset to_numeric to_datetime to_timedeltaFlagsGrouper factorizeuniqueNamedAggarray Categoricalset_eng_float_formatSeries DataFrame)col) SparseDtype) infer_freq)offsets)eval)concatlreshapemelt wide_to_longmerge merge_asof merge_orderedcrosstabpivot pivot_table get_dummies from_dummiescutqcut)apiarrayserrorsioplottingtseries)testing) show_versions) ExcelFile ExcelWriter read_excelread_csvread_fwf read_table read_pickle to_pickleHDFStoreread_hdfread_sqlread_sql_queryread_sql_tableread_clipboard read_parquetread_orc read_feather read_htmlread_xml read_json read_stataread_sas read_spss read_iceberg)json_normalize)testF) __version____git_version__T) get_versionsz closest-tagversionzfull-revisionida pandas - a powerful data analysis and manipulation library for Python ===================================================================== **pandas** is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, **real world** data analysis in Python. Additionally, it has the broader goal of becoming **the most powerful and flexible open source data analysis / manipulation tool available in any language**. It is already well on its way toward this goal. Main Features ------------- Here are just a few of the things that pandas does well: - Easy handling of missing data in floating point as well as non-floating point data. - Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects - Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let `Series`, `DataFrame`, etc. automatically align the data for you in computations. - Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data. - Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects. - Intelligent label-based slicing, fancy indexing, and subsetting of large data sets. - Intuitive merging and joining data sets. - Flexible reshaping and pivoting of data sets. - Hierarchical labeling of axes (possible to have multiple labels per tick). - Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultrafast HDF5 format. - Time series-specific functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging. rr rr?rr#rBr5r(rr^r_r9rrr:rfr"r*r rrrr3rr&r%r+r=r,rr)r$rArDrr.r'r0rrrrrVr>rWr2rCrHrOrTr1r rXrGr;rSrRrrEr4rYrrrvrIrJrLrMrNr r!rFr r r-rPrQrZrUrkrar`rnrbrgrorurqrmrlrdrsrtrhrirjrrrcrprr@rr]rwr\r/r7r6rer8r[r<rK)numpydateutil) __future__r __docformat___hard_dependencies _dependency __import__ ImportError_e pandas.compatr _is_numpy_dev_errname_modulepandas._configrrrr r r pandas.core.config_initpandaspandas.core.apir r rrrrrrrrrrrrrrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2r3r4r5r6r7r8r9r:r;r<r=r>r?r@rArBpandas.core.colrCpandas.core.dtypes.dtypesrDpandas.tseries.apirEpandas.tseriesrFpandas.core.computation.apirGpandas.core.reshape.apirHrIrJrKrLrMrNrOrPrQrRrSrTrUrVrWrXrYrZr[r\pandas.util._print_versionsr] pandas.io.apir^r_r`rarbrcrdrerfrgrhrirjrkrlrmrnrorprqrrrsrtrupandas.io.json._normalizervpandas.util._testerrw_built_with_mesonpandas._version_mesonrxrypandas._versionrzvget__doc____all__X/Users/mibo/.openclaw/workspace/.venv-ak/lib/python3.14/site-packages/pandas/__init__.pyrs`"" +%K;&  >>>>>>>>>>>>>>>~ 1)",">=5B5$  & Vs s s s  s   s   s s s s s s s  s s s  !s "#s $ %s &'s ()s *+s ,-s ./s 01s 23s 45s 67s 8 9s :;s < =s >?s @As BCs D Es FGs HIs JKs LMs NOs PQs RSs TUs VWs X Ys Z [s \ ]s ^_s ` as b cs des f gs his jks l ms n os pqs rss tus vws xys z{s | }s ~ s @ As BCs DEs F Gs H Is JKs LMs N Os PQs RSs TUs VWs XYs Z [s \]s ^_s ` as bcs des fgs his jks lms nos pqs rss tus vws xys z{s |}s ~s @As BCs DEs FGs HIs JKs LMs NOs PQs R Ss TUs VWs XYs Z[s \]s ^_s `as b cs des E 3K=A4 4  iiG  y!+ +   @,A%% q|4Kee-.Oa sAH=I J=I III> I99I>AKK