w:iY\bSrSSKJr SSKrSSKrSSKrSSKJrJrJ r SSK r SSK J r J r SSK Jr SSKJr SSKJr SS KJr SS KJr SS KJr SS KJr SS KJrJr SSKJr SSK J!r! SSK"J#r#J$r$J%r%J&r&J'r' \(aSSK(J)r)J*r*J+r+J,r,J-r- SSjr.S S!Sjjr/"SS5r0"SS\05r1"SS\05r2\"\SS9S"S#Sjj5r3\"\SS9SSS\Rh\RhSS4S$Sjj5r5g)%zparquet compat ) annotationsN) TYPE_CHECKINGAnyLiteral)catch_warningsfilterwarnings) _get_option)lib)import_optional_dependencyAbstractMethodError)doc)find_stack_level)check_dtype_backend) DataFrame get_option) _shared_docs)arrow_table_to_pandas) IOHandles get_handle is_fsspec_urlis_urlstringify_path) DtypeBackendFilePath ReadBufferStorageOptions WriteBufferBaseImplc2US:Xa [S5nUS:Xa-[[/nSnUH nU"5s $ [SU35eUS:Xa [5$US:Xa [5$[ S 5e![anUS[ U5-- nSnAMiSnAff=f) zreturn our implementationautozio.parquet.enginez - NzUnable to find a usable engine; tried using: 'pyarrow', 'fastparquet'. A suitable version of pyarrow or fastparquet is required for parquet support. Trying to import the above resulted in these errors:pyarrow fastparquetz.engine must be one of 'pyarrow', 'fastparquet')r PyArrowImplFastParquetImpl ImportErrorstr ValueError)engineengine_classes error_msgs engine_classerrs T/srv/projetos/modelo_ml_acdoc/venv/lib/python3.13/site-packages/pandas/io/parquet.py get_enginer04s /0 %7 *L 1#~%+  C l    } =   E FF% 1gC00  1sA00 B:BBstorage_optionsc[U5nUb[SSS9n[SSS9nUb-[XR5(aU(a [ S5eOIUb%[XR R 5(aO![S[U5R35e[U5(aqUcnUc4[S5n[S5nURRU5upUc3[S5nURR"U40U=(d 0D6upO(U(a![!U5(aUS :wa [S 5eSn U(dYU(dR[U["5(a=[$R&R)U5(d[+XSS US 9n SnU R,nXYU4$![UR4a Nf=f) zFile handling for PyArrow.Nz pyarrow.fsignore)errorsfsspecz8storage_options not supported with a pyarrow FileSystem.z9filesystem must be a pyarrow or fsspec FileSystem, not a r#rbz8storage_options passed with buffer, or non-supported URLFis_textr1)rr isinstance FileSystemNotImplementedErrorspecAbstractFileSystemr)type__name__rfrom_uri TypeError ArrowInvalidcore url_to_fsrr(ospathisdirrhandle) rFfsr1modeis_dirpath_or_handlepa_fsr5pahandless r/_get_path_or_handlerPVs$D)N ~*<I+HXF  B0@0@!A!A)N Jr;;3Q3Q$R$R b**+- ^$$  "+I6B.|%>t%D" :/9F!'!6!6"#2#8b" B &"8"8DDLSTTG  ~s + + n--  %   B &&7r/  s F++GGc@\rSrSr\SSj5rSSjrS S SjjrSrg) rcD[U[5(d [S5eg)Nz+to_parquet only supports IO with DataFrames)r9rr))dfs r/validate_dataframeBaseImpl.validate_dataframes "i((JK K)c [U5eNr )selfrTrF compressionkwargss r/writeBaseImpl.write !$''rWNc [U5erYr )rZrFcolumnsr\s r/read BaseImpl.readr_rW)rTrreturnNone)rTrrY)rer) r? __module__ __qualname____firstlineno__ staticmethodrUr]rb__static_attributes__rdrWr/rrs%LL(((rWc\rSrSrSSjrS S SjjrSSS\RSS4S SjjrSr g) r%c4[SSS9 SSKnSSKnXlg)Nr#z(pyarrow is required for parquet support.extrar)r pyarrow.parquet(pandas.core.arrays.arrow.extension_typesapi)rZr#pandass r/__init__PyArrowImpl.__init__s" G   8rWNc URU5 SURSS50n UbXIS'URRR"U40U D6n UR (aOS[ R"UR 50n U RRn 0U EU En U RU 5n [UUUSUSLS9upn[U[R5(a|[US5(ak[UR ["[$45(aF[UR [$5(aUR R'5nO UR nUb-URR(R*"U U4UUUS.UD6 O+URR(R,"U U4UUS.UD6 UbUR/5 gg!UbUR/5 ff=f) Nschemapreserve_index PANDAS_ATTRSwb)r1rJrKname)r[partition_cols filesystem)r[r~)rUpoprsTable from_pandasattrsjsondumpsrxmetadatareplace_schema_metadatarPr9ioBufferedWriterhasattrr|r(bytesdecodeparquetwrite_to_dataset write_tableclose)rZrTrFr[indexr1r}r~r\from_pandas_kwargstable df_metadataexisting_metadatamerged_metadatarLrOs r/r]PyArrowImpl.writes #.6 8T8R-S  38/ 0**2D1CD 88)4::bhh+?@K % 5 5 B!2BkBO11/BE.A  +!- / + ~r'8'8 9 9//>..e ==.--u55!/!4!4!;!;!=!/!4!4 )  11"!,#1)     ,,"!,)   " #w" #s "AGG(Fc SUS'0n [SSS9n U S:XaSU S'[UUUSS9upnURRR"U 4UUUS .UD6n [ 5 [ S S [5 [U UU S 9nSSS5 U S:XaWRSS S9nU RR(aNSU RR;a4U RRSn[R"U5WlWU bU R5 $$!,(df  N=f!U bU R5 ff=f)NTuse_pandas_metadatazmode.data_manager)silentarray split_blocksr6)r1rJ)rar~filtersr3zmake_block is deprecated) dtype_backendto_pandas_kwargsF)copys PANDAS_ATTRS)r rPrsr read_tablerrDeprecationWarningr _as_managerrxrrloadsrr)rZrFraruse_nullable_dtypesrr1r~r\rmanagerrLrOpa_tableresultrs r/rbPyArrowImpl.readsV)-$%1$? g /3 ^ ,.A  + / +  xx''22%   H !.& /"/%5 "'!++G%+@''"hoo&>&>>"*//":":?"KK#'::k#:FL" #+"!*" #s$5D-"D?BD- D*&D--ErsrerfsnappyNNNN)rTrrFzFilePath | WriteBuffer[bytes]r[ str | Noner bool | Noner1StorageOptions | Noner}list[str] | Nonererf)rboolrDtypeBackend | lib.NoDefaultr1rrer) r?rgrhrirur]r no_defaultrbrkrdrWr/r%r%s #+!15+/@ @ ,@  @  @ / @ )@  @ J$)69nn157 " 7 4 7 /7  7 7 rWr%c`\rSrSrSSjrSS SjjrS S SjjrSrg) r&i*c$[SSS9nXlg)Nr$z,fastparquet is required for parquet support.ro)r rs)rZr$s r/ruFastParquetImpl.__init__+s1 !O rWNc ^^ URU5 SU;aUb [S5eSU;aURS5nUbSUS'Ub [S5e[ U5n[ U5(a[ S5m U U4SjUS'OT(a [S 5e[S S 9 URR"UU4UUUS .UD6 SSS5 g!,(df  g=f) N partition_onzYCannot use both partition_on and partition_cols. Use partition_cols for partitioning datahive file_scheme9filesystem is not implemented for the fastparquet engine.r5cZ>TR"US40T=(d 0D6R5$)Nr{)open)rF_r5r1s r/'FastParquetImpl.write..Vs,&++d3.4"3df3rW open_withz?storage_options passed with file object or non-fsspec file pathT)record)r[ write_indexr) rUr)rr;rrr rrsr]) rZrTrFr[rr}r1r~r\r5s ` @r/r]FastParquetImpl.write3s # V #(BK  V ##ZZ7N  %$*F= !  !%K  d#   /9F#F; Q 4 ( HHNN (!+     ) ( (s !#C  Cc 0nURSS5nURS[R5n SUS'U(a [S5eU [RLa [S5eUb [ S5e[ U5nSn [ U5(a6[S5n U R"US 40U=(d 0D6RUS 'OQ[U[5(a<[RRU5(d[US SUS 9n U R nUR"R$"U40UD6n U R&"S X#S .UD6U bU R)5 $$!U bU R)5 ff=f)NrFr pandas_nullszNThe 'use_nullable_dtypes' argument is not supported for the fastparquet enginezHThe 'dtype_backend' argument is not supported for the fastparquet enginerr5r6rIr7)rarrd)rr rr)r;rrr rrIr9r(rErFrGrrHrs ParquetFile to_pandasr) rZrFrarr1r~r\parquet_kwargsrrrOr5 parquet_files r/rbFastParquetImpl.readhsn*,$jj)>F ?CNNC ).~& %   .%   !%K d#   /9F#);;tT#Uo>SQS#U#X#XN4 c " "277==+>+>!dE?G>>D 88//GGL))U'UfU" #w" #s 0E""E8rrr)rTrr[z*Literal['snappy', 'gzip', 'brotli'] | Noner1rrerf)NNNN)r1rrer)r?rgrhrirur]rbrkrdrWr/r&r&*ssCK153 3@ 3/3 3p15 0 / 0  0 0 rWr&)r1r!c [U[5(aU/n[U5n Uc[R"5OUn U R "UU 4UUUUUS.UD6 Uc1[U [R5(deU R 5$g)a Write a DataFrame to the parquet format. Parameters ---------- df : DataFrame path : str, path object, file-like object, or None, default None String, path object (implementing ``os.PathLike[str]``), or file-like object implementing a binary ``write()`` function. If None, the result is returned as bytes. If a string, it will be used as Root Directory path when writing a partitioned dataset. The engine fastparquet does not accept file-like objects. engine : {{'auto', 'pyarrow', 'fastparquet'}}, default 'auto' Parquet library to use. If 'auto', then the option ``io.parquet.engine`` is used. The default ``io.parquet.engine`` behavior is to try 'pyarrow', falling back to 'fastparquet' if 'pyarrow' is unavailable. When using the ``'pyarrow'`` engine and no storage options are provided and a filesystem is implemented by both ``pyarrow.fs`` and ``fsspec`` (e.g. "s3://"), then the ``pyarrow.fs`` filesystem is attempted first. Use the filesystem keyword with an instantiated fsspec filesystem if you wish to use its implementation. compression : {{'snappy', 'gzip', 'brotli', 'lz4', 'zstd', None}}, default 'snappy'. Name of the compression to use. Use ``None`` for no compression. index : bool, default None If ``True``, include the dataframe's index(es) in the file output. If ``False``, they will not be written to the file. If ``None``, similar to ``True`` the dataframe's index(es) will be saved. However, instead of being saved as values, the RangeIndex will be stored as a range in the metadata so it doesn't require much space and is faster. Other indexes will be included as columns in the file output. partition_cols : str or list, optional, default None Column names by which to partition the dataset. Columns are partitioned in the order they are given. Must be None if path is not a string. {storage_options} filesystem : fsspec or pyarrow filesystem, default None Filesystem object to use when reading the parquet file. Only implemented for ``engine="pyarrow"``. .. versionadded:: 2.1.0 kwargs Additional keyword arguments passed to the engine Returns ------- bytes if no path argument is provided else None N)r[rr}r1r~)r9r(r0rBytesIOr]getvalue) rTrFr*r[rr1r}r~r\impl path_or_bufs r/ to_parquetrsB.#&&() f DAESWKJJ   %'    |+rzz2222##%%rWc [U5n U[RLa/Sn USLaU S- n [R"U [ [ 5S9 OSn[U5 U R"U4UUUUUUS.UD6$)a Load a parquet object from the file path, returning a DataFrame. Parameters ---------- path : str, path object or file-like object String, path object (implementing ``os.PathLike[str]``), or file-like object implementing a binary ``read()`` function. The string could be a URL. Valid URL schemes include http, ftp, s3, gs, and file. For file URLs, a host is expected. A local file could be: ``file://localhost/path/to/table.parquet``. A file URL can also be a path to a directory that contains multiple partitioned parquet files. Both pyarrow and fastparquet support paths to directories as well as file URLs. A directory path could be: ``file://localhost/path/to/tables`` or ``s3://bucket/partition_dir``. engine : {{'auto', 'pyarrow', 'fastparquet'}}, default 'auto' Parquet library to use. If 'auto', then the option ``io.parquet.engine`` is used. The default ``io.parquet.engine`` behavior is to try 'pyarrow', falling back to 'fastparquet' if 'pyarrow' is unavailable. When using the ``'pyarrow'`` engine and no storage options are provided and a filesystem is implemented by both ``pyarrow.fs`` and ``fsspec`` (e.g. "s3://"), then the ``pyarrow.fs`` filesystem is attempted first. Use the filesystem keyword with an instantiated fsspec filesystem if you wish to use its implementation. columns : list, default=None If not None, only these columns will be read from the file. {storage_options} .. versionadded:: 1.3.0 use_nullable_dtypes : bool, default False If True, use dtypes that use ``pd.NA`` as missing value indicator for the resulting DataFrame. (only applicable for the ``pyarrow`` engine) As new dtypes are added that support ``pd.NA`` in the future, the output with this option will change to use those dtypes. Note: this is an experimental option, and behaviour (e.g. additional support dtypes) may change without notice. .. deprecated:: 2.0 dtype_backend : {{'numpy_nullable', 'pyarrow'}}, default 'numpy_nullable' Back-end data type applied to the resultant :class:`DataFrame` (still experimental). Behaviour is as follows: * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame` (default). * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype` DataFrame. .. versionadded:: 2.0 filesystem : fsspec or pyarrow filesystem, default None Filesystem object to use when reading the parquet file. Only implemented for ``engine="pyarrow"``. .. versionadded:: 2.1.0 filters : List[Tuple] or List[List[Tuple]], default None To filter out data. Filter syntax: [[(column, op, val), ...],...] where op is [==, =, >, >=, <, <=, !=, in, not in] The innermost tuples are transposed into a set of filters applied through an `AND` operation. The outer list combines these sets of filters through an `OR` operation. A single list of tuples can also be used, meaning that no `OR` operation between set of filters is to be conducted. Using this argument will NOT result in row-wise filtering of the final partitions unless ``engine="pyarrow"`` is also specified. For other engines, filtering is only performed at the partition level, that is, to prevent the loading of some row-groups and/or files. .. versionadded:: 2.1.0 **kwargs Any additional kwargs are passed to the engine. Returns ------- DataFrame See Also -------- DataFrame.to_parquet : Create a parquet object that serializes a DataFrame. Examples -------- >>> original_df = pd.DataFrame( ... {{"foo": range(5), "bar": range(5, 10)}} ... ) >>> original_df foo bar 0 0 5 1 1 6 2 2 7 3 3 8 4 4 9 >>> df_parquet_bytes = original_df.to_parquet() >>> from io import BytesIO >>> restored_df = pd.read_parquet(BytesIO(df_parquet_bytes)) >>> restored_df foo bar 0 0 5 1 1 6 2 2 7 3 3 8 4 4 9 >>> restored_df.equals(original_df) True >>> restored_bar = pd.read_parquet(BytesIO(df_parquet_bytes), columns=["bar"]) >>> restored_bar bar 0 5 1 6 2 7 3 8 4 9 >>> restored_bar.equals(original_df[['bar']]) True The function uses `kwargs` that are passed directly to the engine. In the following example, we use the `filters` argument of the pyarrow engine to filter the rows of the DataFrame. Since `pyarrow` is the default engine, we can omit the `engine` argument. Note that the `filters` argument is implemented by the `pyarrow` engine, which can benefit from multithreading and also potentially be more economical in terms of memory. >>> sel = [("foo", ">", 2)] >>> restored_part = pd.read_parquet(BytesIO(df_parquet_bytes), filters=sel) >>> restored_part foo bar 0 3 8 1 4 9 zYThe argument 'use_nullable_dtypes' is deprecated and will be removed in a future version.TzFUse dtype_backend='numpy_nullable' instead of use_nullable_dtype=True.) stacklevelF)rarr1rrr~) r0r rwarningswarn FutureWarningrrrb) rFr*rar1rrr~rr\rmsgs r/ read_parquetrsr f D#..0 #  $ & X C  c=5E5GH# & 99  '/#    rW)r*r(rer)Nr6F) rFz1FilePath | ReadBuffer[bytes] | WriteBuffer[bytes]rIrr1rrJr(rKrrezVtuple[FilePath | ReadBuffer[bytes] | WriteBuffer[bytes], IOHandles[bytes] | None, Any])Nr!rNNNN)rTrrFz$FilePath | WriteBuffer[bytes] | Noner*r(r[rrrr1rr}rr~rrez bytes | None)rFzFilePath | ReadBuffer[bytes]r*r(rarr1rrzbool | lib.NoDefaultrrr~rrz&list[tuple] | list[list[tuple]] | Nonerer)6__doc__ __future__rrrrEtypingrrrrrrpandas._config.configr pandas._libsr pandas.compat._optionalr pandas.errorsr pandas.util._decoratorsrpandas.util._exceptionsrpandas.util._validatorsrrtrrpandas.core.shared_docsrpandas.io._utilrpandas.io.commonrrrrrpandas._typingrrrrrr0rPrr%r&rrrrdrWr/rs"   .>-'4711GJ.2 <' ;<' <'+<'  <'  <'  <'~ ( (E (E Pn hn b\"34526&-1'+UU .U U U  U + U%UUU6Up\"345 $-10325..6:q &q qq+ q . q 0 qq4qq6qrW