+ ܳj+^RIHt^RIt^RIt^RIHt^RIHtHtH t H t ^RIH t ^RI Ht^RIHt!RR4tR t0RmtRR ltRR ltRR ltRR ltR#))SequenceN) _pandas_api)CodecTable concat_tablesschema)_feather) FeatherErrorcHa]tRt^toRtRRltR RltRtR RltRt Vt R#) FeatherDatasetz Encapsulates details of reading a list of Feather files. Parameters ---------- path_or_paths : List[str] A list of file names validate_schema : bool, default True Check that individual file schemas are all the same / compatible cWnW nR#N)pathsvalidate_schema)self path_or_pathsrs&&&P/home/luca/screen-checking/.venv/lib/python3.14/site-packages/pyarrow/feather.py__init__FeatherDataset.__init__+s " .Nc b\VP^,VR7pV.VnVPVnVPR,FMp\W1R7pVP'dVP W44VPP V4KO \VP4#)z Read multiple feather files as a single pyarrow.Table Parameters ---------- columns : List[str] Names of columns to read from the file Returns ------- pyarrow.Table Content of the file as a table (of columns) columns:NN)_read_table_internalr_tablesrrvalidate_schemasappendr)rr_filpathtables&& r read_tableFeatherDataset.read_table/s$DJJqM7Cv kk JJrNND(?E###%%d2 LL   & # T\\**rcVPPVP4'g)\RV RVP RVP 24hR#)z Schema in z was different. z vs N)requals ValueError)rpiecer!s&&&rrFeatherDataset.validate_schemasHsQ{{!!%,,//z%0B $ }Ju||nFG G0rc FVPVR7PVR7#)aA Read multiple Parquet files as a single pandas DataFrame Parameters ---------- columns : List[str] Names of columns to read from the file use_threads : bool, default True Use multiple threads when converting to pandas Returns ------- pandas.DataFrame Content of the file as a pandas DataFrame (of columns) r) use_threads)r" to_pandas)rrr*s&&&r read_pandasFeatherDataset.read_pandasMs* w/99#:% %r)rrrr)Tr)NT) __name__ __module__ __qualname____firstlineno____doc__rr"rr,__static_attributes____classdictcell__) __classdict__s@rr r s$ /+2G %%rr cVP^8XdR#VP\P!4\P!439d\ RV R24h\ RV RVP R24h)rNzColumn 'zg' exceeds 2GB maximum capacity of a Feather binary column. This restriction may be lifted in the futurez ' of type zU was chunked on conversion to Arrow and cannot be currently written to Feather format) num_chunkstypeextbinarystringr&)namecols&&rcheck_chunked_overflowr>asw ~~ xxCJJL#**,//8D6*001 1 tfJsxxj1@ @  rc V^8Xd\P!R\^R7\P'dQ\P 'd;\ V\PP4'dVP4p\P!V4'dzV^8XdRpMV^8XdRpM \R4h\P!WR7pV^8Xd<\VPP 4FwrWx,p \#W4K MTpV^8Xda\%VP&4\%\)VP&448d \R4hVe \R4hVe \R 4hMGVf \*P,!R 4'dR pM$Ve!V\.9d\R V R \. 24h\0P2!WqVVWER7R# \4dL\ T\64'd4\8P:!T4h \8P<dhi;ihi;i)a Write a pandas.DataFrame to Feather format. Parameters ---------- df : pandas.DataFrame or pyarrow.Table Data to write out as Feather format. dest : str Local destination path. compression : string, default None Can be one of {"zstd", "lz4", "uncompressed"}. The default of None uses LZ4 for V2 files if it is available, otherwise uncompressed. compression_level : int, default None Use a compression level particular to the chosen compressor. If None use the default compression level chunksize : int, default None For V2 files, the internal maximum size of Arrow RecordBatch chunks when writing the Arrow IPC file format. None means use the default, which is currently 64K version : int, default 2 Feather file version. Version 2 is the current. Version 1 is the more limited legacy format. .. deprecated:: 25.0.0 Writing Feather V1 files is deprecated. Use the default ``version=2`` to write Arrow IPC files instead. zFeather V1 files are deprecated as of 25.0.0 and support will be removed in a future version. Use the default version=2 to write Arrow IPC files instead. stacklevelFNz%Version value should either be 1 or 2)preserve_indexz'cannot serialize duplicate column namesz2Feather V1 files do not support compression optionz0Feather V1 files do not support chunksize option lz4_framelz4z compression="z " not supported, must be one of ) compressioncompression_level chunksizeversion)warningswarnDeprecationWarningr have_pandas has_sparse isinstancepdSparseDataFrameto_dense is_data_framer&r from_pandas enumeraternamesr>len column_namessetr is_available_FEATHER_SUPPORTED_CODECSr write_feather Exceptionstrosremoveerror) dfdestrErFrGrHrBr!ir<r=s &&&&&& rr[r[ts:!|  -     " " "2{~~==>>B  $$ a<"N \!NDE E!!"D a<$U\\%7%78h&t19!| u!! "SU-?-?)@%A AFG G  "&' '  &' ' !  5#5#5k#B#BK%!::}[M:''@&ACD D u 1B)2 E  dC   $ 88    s0G--!IH&%I&H>;I=H>>Ic B\WVVR7P!RRV/VB#)aN Read a pandas.DataFrame from Feather format. To read as pyarrow.Table use feather.read_table. Parameters ---------- source : str file path, or file-like object You can use MemoryMappedFile as source, for explicitly use memory map. columns : sequence, optional Only read a specific set of columns. If not provided, all columns are read. use_threads : bool, default True Whether to parallelize reading using multiple threads. If false the restriction is used in the conversion to Pandas as well as in the reading from Feather format. memory_map : boolean, default False Use memory mapping when opening file on disk, when source is a str. **kwargs Additional keyword arguments passed on to `pyarrow.Table.to_pandas`. Returns ------- df : pandas.DataFrame The contents of the Feather file as a pandas.DataFrame r memory_mapr*r*)rr+)sourcerr*rfkwargss&&&&,r read_featherrjs<6 !J !!* + N7B NFL NOrc\P!WVR7pVP^8d\P!R\ ^R7VfVP 4#\V\4'g.\RP\V4P44hVUu.uFp\V4NK pp\\RV44'dVPV4pMY\\RV44'dVP!V4pM+VUu.uFqPNK p p\RV RV 24hVP^8dV#\#\%V44V8XdV#VP'V4#uupiuupi) z Internal implementation for reading a Feather file as a pyarrow.Table. Emits a deprecation warning if the file is a legacy Feather V1 file. )use_memory_mapr*zFeather V1 files are deprecated as of 25.0.0 and support will be removed in a future version. Consider rewriting this file in the Arrow IPC file format (Feather V2).r@z&Columns must be a sequence but, got {}cV\8H#r)intts&r&_read_table_internal.. scrcV\8H#r)r]ros&rrqrrs18rz.Columns must be indices or names. Got columns z of types )r FeatherReaderrHrIrJrKreadrNr TypeErrorformatr8r.allmap read_indices read_namessortedrXselect) rhrrfr*readercolumn column_typesr!rpcolumn_type_namess &&&& rrrsg  # #{DF~~  9   {{} gx ( (@W 6 679 9077wVDLwL7 3!< 011##G, S#\ 2 3 3!!'*1=>AZZ>''.iz:K9LNO O~~ G  ( ||G$$%8 ?s "E>Fc\WVVR7#)aO Read a pyarrow.Table from Feather format Parameters ---------- source : str file path, or file-like object You can use MemoryMappedFile as source, for explicitly use memory map. columns : sequence, optional Only read a specific set of columns. If not provided, all columns are read. memory_map : boolean, default False Use memory mapping when opening file on disk, when source is a str use_threads : bool, default True Whether to parallelize reading using multiple threads. Returns ------- table : pyarrow.Table The contents of the Feather file as a pyarrow.Table re)r)rhrrfr*s&&&&rr"r"!s* +5,7 99r>rDzstd uncompressed)NNN)NTF)NFT)collections.abcrr^rIpyarrow.pandas_compatr pyarrow.librrrrlibr9pyarrowr pyarrow._featherr r r>rZr[rjrr"rgrrrsW&% -00)?%?%D  <\~O@+%\9r