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object is not subscriptablehasattr(): attribute name must be stringpyarrow._parquet.__pyx_unpickle_SortingColumn__set_statepyarrow._parquet.ParquetLogicalType.__setstate_cython__pyarrow._parquet.ParquetReader.__setstate_cython__pyarrow._parquet.ColumnChunkMetaData.__setstate_cython__.0local variable '%s' referenced before assignmentgenexprpyarrow._parquet.ParquetWriter.__setstate_cython__pyarrow._parquet.compression_from_nameto_dictpyarrow._parquet.SortingColumn.to_dictpyarrow._parquet.SortingColumn.__reduce_cython__pyarrow._parquet.RowGroupMetaData.__reduce__pyarrow._parquet.ParquetSchema.__reduce__pyarrow._parquet.ParquetWriter.dictionary_pagesize_limit.__get__pyarrow._parquet.ParquetWriter.write_batch_size.__get__pyarrow._parquet.ParquetWriter.data_page_size.__get__pyarrow._parquet.ParquetWriter.row_group_size.__get__pyarrow._parquet.ColumnSchema.scale.__get__pyarrow._parquet.ColumnSchema.precision.__get__pyarrow._parquet.ColumnSchema.length.__get__pyarrow._parquet.ColumnSchema.logical_type.__get__pyarrow._parquet.ColumnSchema.max_repetition_level.__get__pyarrow._parquet.ColumnSchema.max_definition_level.__get__pyarrow._parquet.FileMetaData.num_row_groups.__get__pyarrow._parquet.FileMetaData.num_rows.__get__pyarrow._parquet.FileMetaData.num_columns.__get__pyarrow._parquet.FileMetaData.serialized_size.__get__pyarrow._parquet.RowGroupMetaData.total_byte_size.__get__pyarrow._parquet.RowGroupMetaData.num_rows.__get__pyarrow._parquet.RowGroupMetaData.num_columns.__get__pyarrow._parquet.SortingColumn.column_index.__get__pyarrow._parquet.ColumnChunkMetaData.total_uncompressed_size.__get__pyarrow._parquet.ColumnChunkMetaData.total_compressed_size.__get__pyarrow._parquet.ColumnChunkMetaData.data_page_offset.__get__pyarrow._parquet.ColumnChunkMetaData.has_dictionary_page.__get__pyarrow._parquet.compression_name_from_enumpyarrow._parquet.ColumnChunkMetaData.compression.__get__pyarrow._parquet.ColumnChunkMetaData.num_values.__get__pyarrow._parquet.ColumnChunkMetaData.file_offset.__get__pyarrow._parquet._box_byte_arraypyarrow._parquet._box_flbapyarrow._parquet._cast_statistic_raw_minpyarrow._parquet.Statistics.min_raw.__get__pyarrow._parquet._cast_statistic_raw_maxpyarrow._parquet.Statistics.max_raw.__get__string.to_py.__pyx_convert_PyBytes_string_to_py_6libcpp_6string_std__in_stringpyarrow._parquet.ParquetSchema.__hash__pyarrow._parquet.ColumnSchema.name.__get__pyarrow._parquet.ParquetLogicalType.__str__pyarrow._parquet.FileMetaData.created_by.__get__pyarrow._parquet.ColumnChunkMetaData.file_path.__get__to_jsonpyarrow._parquet.ParquetLogicalType.to_jsonequalspyarrow._parquet.FileMetaData.equalsiter_batchespyarrow._parquet.ParquetReader.iter_batchestupleExpected %s, got %.200spyarrow._parquet.SortingColumn.__setstate_cython__pyarrow._parquet.ColumnChunkMetaData.equalspyarrow._parquet.RowGroupMetaData.equalspyarrow._parquet.ColumnSchema.equalspyarrow._parquet.ParquetSchema.equalscannot import name %SModule '_parquet' has already been imported. Re-initialisation is not supported.cython_runtimedoes not matchpyarrow._parquetcompile time Python version %d.%d of module '%.100s' %s runtime version %d.%dstd::shared_ptr<parquet::WriterProperties>  (struct __pyx_opt_args_7pyarrow_8_parquet__create_writer_properties *__pyx_optional_args)_create_writer_propertiesstd::shared_ptr<parquet::ArrowWriterProperties>  (struct __pyx_opt_args_7pyarrow_8_parquet__create_arrow_writer_properties *__pyx_optional_args)_create_arrow_writer_properties_Weakrefableint (arrow::Status const &)check_status arrow::MemoryPool *(struct __pyx_obj_7pyarrow_3lib_MemoryPool *)maybe_unbox_memory_poolPyObject *(PyObject *, bool, std::shared_ptr< arrow::io::RandomAccessFile>  *)get_readerPyObject *(PyObject *, std::shared_ptr< arrow::io::OutputStream>  *)get_writerenum  arrow::TimeUnit::type (PyObject *)string_to_timeunitstd::shared_ptr< arrow::KeyValueMetadata const >  (PyObject *)pyarrow_unwrap_metadataPyObject *(std::shared_ptr< arrow::Schema>  const &)pyarrow_wrap_schemaPyObject *(std::shared_ptr< arrow::Scalar>  const &)pyarrow_wrap_scalarPyObject *(std::shared_ptr< arrow::ChunkedArray>  const &)pyarrow_wrap_chunked_arrayPyObject *(std::shared_ptr< arrow::RecordBatch>  const &)pyarrow_wrap_batchPyObject *(std::shared_ptr< arrow::Table>  const &)pyarrow_wrap_tablestd::shared_ptr< arrow::Schema>  (PyObject *)pyarrow_unwrap_schemacollections.abcCython module failed to register with collections.abc modulebackports_abcinit pyarrow._parquet__pyx_unpickle_SortingColumnpyarrow._parquet.__pyx_unpickle_SortingColumnpyarrow._parquet.Statistics.equalspyarrow._parquet.RowGroupMetaData.__eq__pyarrow._parquet.ColumnSchema.__eq__pyarrow._parquet.Statistics.__eq__pyarrow._parquet.ColumnChunkMetaData.__eq__pyarrow._parquet.FileMetaData.__eq__pyarrow._parquet.ParquetSchema.__eq__pyarrow._parquet.converted_type_name_from_enumpyarrow._parquet.Statistics.converted_type.__get__pyarrow._parquet.ColumnSchema.converted_type.__get__columnpyarrow._parquet.ParquetSchema.columnread_row_grouppyarrow._parquet.ParquetReader.read_row_grouppyarrow._parquet.ParquetSchema.__repr__pyarrow._parquet.RowGroupMetaData.columnpyarrow._parquet.logical_type_name_from_enumpyarrow._parquet.ParquetLogicalType.type.__get__pyarrow._parquet.ColumnChunkMetaData.to_dictkeywords must be stringsunbound method %.200S() needs an argumentpyarrow._parquet.encoding_enum_from_namestrings are too large to concatpyarrow._parquet.encoding_name_from_enumpyarrow._parquet.FileMetaData.format_version.__get__throwpyarrow._parquet.check_compression_namepyarrow._parquet.RowGroupMetaData.to_dictpyarrow._parquet.Statistics.to_dictwrapcfunc.to_py.__Pyx_CFunc_7pyarrow_8_parquet_object__lParenParquetEncoding__rParen_to_py_9encoding_.wraprow_grouppyarrow._parquet.FileMetaData.row_grouppyarrow._parquet.ColumnChunkMetaData.__repr__pyarrow._parquet.ColumnSchema.__repr__pyarrow._parquet.physical_type_name_from_enumpyarrow._parquet.Statistics.physical_type.__get__pyarrow._parquet.ColumnSchema.physical_type.__get__pyarrow._parquet.ColumnChunkMetaData.physical_type.__get__pyarrow._parquet.RowGroupMetaData.__cinit__pyarrow._parquet.FileMetaData.to_dictpyarrow._parquet.ColumnSchema.__cinit__column_name_idxpyarrow._parquet.ParquetReader.column_name_idxCannot convert %.200s to %.200spyarrow._parquet._create_arrow_writer_propertiesclosepyarrow._parquet.ParquetReader.closepyarrow._parquet.ParquetWriter.closewrite_tablepyarrow._parquet.ParquetWriter.write_tabletablepyarrow._parquet.ParquetReader.schema_arrow.__get__pyarrow/_parquet.pxdpyarrow._parquet.FileDecryptionProperties.wrappyarrow._parquet.FileEncryptionProperties.wrappyarrow._parquet._cast_statisticspyarrow._parquet.Statistics.max.__get__pyarrow._parquet.Statistics.min.__get__to_arrow_schemapyarrow._parquet.ParquetSchema.to_arrow_schemapyarrow._parquet.ColumnSchema.path.__get__pyarrow._parquet.ColumnChunkMetaData.path_in_schema.__get__read_columnpyarrow._parquet.ParquetReader.read_columnadd_key_value_metadatapyarrow._parquet.ParquetWriter.add_key_value_metadata_reconstruct_filemetadatapyarrow._parquet._reconstruct_filemetadataserializedpyarrow._parquet.ParquetWriter.metadata.__get__pyarrow._parquet.FileMetaData.__reduce__append_row_groupspyarrow._parquet.FileMetaData.append_row_groupspyarrow._parquet.ColumnChunkMetaData.statistics.__get__openpyarrow._parquet.ParquetReader.opensourceArgument '%.200s' must not be Nonemetadatadecryption_propertiespyarrow._parquet.ParquetReader.column_paths.__get__pyarrow._parquet.RowGroupMetaData.sorting_columns.__get__pyarrow._parquet.FileMetaData.write_metadata_filewrite_metadata_filescan_contentspyarrow._parquet.ParquetReader.scan_contentsread_allpyarrow._parquet.ParquetReader.read_allread_row_groupspyarrow._parquet.ParquetReader.read_row_groupsrow_groupsmap.to_py.__pyx_convert_unordered_map_to_py_std_3a__3a_string____std_3a__3a_stringpyarrow._parquet.FileMetaData.metadata.__get__pyarrow._parquet._convert_sorting_columns.genexprvector.from_py.__pyx_convert_vector_from_py_struct____parquet_3a__3a_SortingColumna mappingFromPyStructUtility.__pyx_convert__from_py_struct____parquet_3a__3a_SortingColumnpyarrow._parquet._convert_sorting_columnsunicodeCan't use dictionary encoding as fallback encodingpyarrow._parquet._create_writer_propertiespyarrow._parquet._name_to_index_mapto_orderingpyarrow._parquet.SortingColumn.to_orderingitems'NoneType' object has no attribute '%.30s'from_orderingpyarrow._parquet.SortingColumn.from_orderingpyarrow._parquet.ParquetWriter.__cinit__pyarrow._parquet.ParquetReader._set_read_dictionary_cython_3_0_10.generator__name__name of the generator__qualname__qualified name of the generatorgi_frameFrame of the generatorgi_runninggi_yieldfromobject being iterated by 'yield from', or Nonegi_code__module__sendsend(arg) -> send 'arg' into generator,
return next yielded value or raise StopIteration.throw(typ[,val[,tb]]) -> raise exception in generator,
return next yielded value or raise StopIteration.close() -> raise GeneratorExit inside generator._cython_3_0_10.cython_function_or_methodfunc_doc__doc__func_namefunc_dict__dict__func_globals__globals__func_closure__closure__func_code__code__func_defaults__defaults____kwdefaults____annotations___is_coroutinepyarrow._parquet.__pyx_scope_struct____Pyx_CFunc_7pyarrow_8_parquet_object__lParenParquetEncoding__rParen_to_py_9encoding_pyarrow._parquet.__pyx_scope_struct_1_genexprpyarrow._parquet.__pyx_scope_struct__iter_batchespyarrow._parquet.ParquetWriteruse_dictionaryuse_deprecated_int96_timestampsuse_byte_stream_splitcolumn_encodingcoerce_timestampsallow_truncated_timestampscompressioncompression_leveldata_page_versionuse_compliant_nested_typeversionwrite_statisticswriter_engine_versionrow_group_sizedata_page_sizeencryption_propertieswrite_batch_sizedictionary_pagesize_limitstore_schemastore_decimal_as_integerpyarrow._parquet.ParquetReadercolumn_pathsschema_arrownum_row_groupsclosed_column_idx_map_column_idx_map: objectpyarrow._parquet.ColumnSchemaSchema for a single column.Name of field (str).pathNested path to field, separated by periods (str).max_definition_levelMaximum definition level (int).max_repetition_levelMaximum repetition level (int).physical_typeName of physical type (str).logical_typeLogical type of column (:class:`ParquetLogicalType`).converted_typeLegacy converted type (str or None).lengthArray length if fixed length byte array type, None otherwise (int or None).precisionPrecision if decimal type, None otherwise (int or None).scaleScale if decimal type, None otherwise (int or None).__repr__pyarrow._parquet.SortingColumnSortingColumn(int column_index, bool descending=False, bool nulls_first=False)

    Sorting specification for a single column.

    Returned by :meth:`RowGroupMetaData.sorting_columns` and used in
    :class:`ParquetWriter` to specify the sort order of the data.

    Parameters
    ----------
    column_index : int
        Index of column that data is sorted by.
    descending : bool, default False
        Whether column is sorted in descending order.
    nulls_first : bool, default False
        Whether null values appear before valid values.

    Notes
    -----

    Column indices are zero-based, refer only to leaf fields, and are in
    depth-first order. This may make the column indices for nested schemas
    different from what you expect. In most cases, it will be easier to
    specify the sort order using column names instead of column indices
    and converting using the ``from_ordering`` method.

    Examples
    --------

    In other APIs, sort order is specified by names, such as:

    >>> sort_order = [('id', 'ascending'), ('timestamp', 'descending')]

    For Parquet, the column index must be used instead:

    >>> import pyarrow.parquet as pq
    >>> [pq.SortingColumn(0), pq.SortingColumn(1, descending=True)]
    [SortingColumn(column_index=0, descending=False, nulls_first=False), SortingColumn(column_index=1, descending=True, nulls_first=False)]

    Convert the sort_order into the list of sorting columns with
    ``from_ordering`` (note that the schema must be provided as well):

    >>> import pyarrow as pa
    >>> schema = pa.schema([('id', pa.int64()), ('timestamp', pa.timestamp('ms'))])
    >>> sorting_columns = pq.SortingColumn.from_ordering(schema, sort_order)
    >>> sorting_columns
    (SortingColumn(column_index=0, descending=False, nulls_first=False), SortingColumn(column_index=1, descending=True, nulls_first=False))

    Convert back to the sort order with ``to_ordering``:

    >>> pq.SortingColumn.to_ordering(schema, sorting_columns)
    ((('id', 'ascending'), ('timestamp', 'descending')), 'at_end')

    See Also
    --------
    RowGroupMetaData.sorting_columns
    column_index"Index of column data is sorted by (int).descendingWhether column is sorted in descending order (bool).nulls_firstWhether null values appear before valid values (bool).pyarrow._parquet.ParquetLogicalTypeLogical type of parquet type.Name of the logical type (str).pyarrow._parquet.FileDecryptionPropertiesFile-level decryption properties for the low-level APIpyarrow._parquet.StatisticsStatistics for a single column in a single row group.has_min_maxWhether min and max are present (bool).has_null_countWhether null count is present (bool).has_distinct_countWhether distinct count is preset (bool).min_rawMin value as physical type (bool, int, float, or bytes).max_rawMax value as physical type (bool, int, float, or bytes).min
        Min value as logical type.

        Returned as the Python equivalent of logical type, such as datetime.date
        for dates and decimal.Decimal for decimals.
        
        Max value as logical type.

        Returned as the Python equivalent of logical type, such as datetime.date
        for dates and decimal.Decimal for decimals.
        null_countNumber of null values in chunk (int).distinct_countDistinct number of values in chunk (int).num_valuesNumber of non-null values (int).Physical type of column (str).pyarrow._parquet.ColumnChunkMetaDataColumn metadata for a single row group.file_offsetOffset into file where column chunk is located (int).file_pathOptional file path if set (str or None).Total number of values (int).path_in_schemais_stats_setWhether or not statistics are present in metadata (bool).statisticsStatistics for column chunk (:class:`Statistics`).
        Type of compression used for column (str).

        One of 'UNCOMPRESSED', 'SNAPPY', 'GZIP', 'LZO', 'BROTLI', 'LZ4', 'ZSTD',
        or 'UNKNOWN'.
        encodings
        Encodings used for column (tuple of str).

        One of 'PLAIN', 'BIT_PACKED', 'RLE', 'BYTE_STREAM_SPLIT', 'DELTA_BINARY_PACKED',
        'DELTA_LENGTH_BYTE_ARRAY', 'DELTA_BYTE_ARRAY'.
        has_dictionary_pageWhether there is dictionary data present in the column chunk (bool).dictionary_page_offsetOffset of dictionary page relative to column chunk offset (int).data_page_offsetOffset of data page relative to column chunk offset (int).has_index_pageNot yet supported.index_page_offsettotal_compressed_sizeCompressed size in bytes (int).total_uncompressed_sizeUncompressed size in bytes (int).has_offset_indexWhether the column chunk has an offset indexhas_column_indexWhether the column chunk has a column indexpyarrow._parquet.RowGroupMetaDataMetadata for a single row group.num_columnsNumber of columns in this row group (int).num_rowsNumber of rows in this row group (int).total_byte_sizeTotal byte size of all the uncompressed column data in this row group (int).sorting_columnsColumns the row group is sorted by (tuple of :class:`SortingColumn`)).pyarrow._parquet.FileMetaDataParquet metadata for a single file.Schema of the file (:class:`ParquetSchema`).serialized_sizeSize of the original thrift encoded metadata footer (int).Number of columns in file (int).Total number of rows in file (int).Number of row groups in file (int).format_version
        Parquet format version used in file (str, such as '1.0', '2.4').

        If version is missing or unparsable, will default to assuming '2.6'.
        created_by
        String describing source of the parquet file (str).

        This typically includes library name and version number. For example, Arrow 7.0's
        writer returns 'parquet-cpp-arrow version 7.0.0'.
        Additional metadata as key value pairs (dict[bytes, bytes]).pyarrow._parquet.ParquetSchemaA Parquet schema.namesName of each field (list of str).pyarrow._parquet.FileEncryptionPropertiesFile-level encryption properties for the low-level API
#.9DOZep\Za,*(&$" 
ØÄ°ŠxfTC3#õçÙÌÀµ­¦ŸUì
!#%')+-/13579;=?AEGIKMC{0}
{1}{0}
  created_by: {1}
  num_columns: {2}
  num_rows: {3}
  num_row_groups: {4}
  format_version: {5}
  serialized_size: {6}{0}
  file_offset: {1}
  file_path: {2}
  physical_type: {3}
  num_values: {4}
  path_in_schema: {5}
  is_stats_set: {6}
  statistics:
{7}
  compression: {8}
  encodings: {9}
  has_dictionary_page: {10}
  dictionary_page_offset: {11}
  data_page_offset: {12}
  total_compressed_size: {13}
  total_uncompressed_size: {14}{0}
  num_columns: {1}
  num_rows: {2}
  total_byte_size: {3}
  sorting_columns: {4}{0} out of bounds1.02.02.42.6ArrowExceptionBIT_PACKEDBOOLEANBROTLIBSONBYTE_ARRAYBYTE_STREAM_SPLITBufferOutputStreamBuffer size must be larger than zeroColumnChunkMetaDataColumnChunkMetaData.__reduce_cython__ColumnChunkMetaData.__setstate_cython__ColumnChunkMetaData.equalsColumnChunkMetaData.to_dictColumnSchemaColumnSchema.__reduce__ColumnSchema.equalsDATEDECIMALDECIMAL({0}, {1})_DEFAULT_ROW_GROUP_SIZEDELTA_BINARY_PACKEDDELTA_BYTE_ARRAYDELTA_LENGTH_BYTE_ARRAYDOUBLEENUMFIXED_LEN_BYTE_ARRAYFIXED_LEN_BYTE_ARRAY(length={0})FLOATFLOAT16FileDecryptionPropertiesFileEncryptionPropertiesFileMetaDataFileMetaData.__reduce__FileMetaData.append_row_groupsFileMetaData.equalsFileMetaData.row_groupFileMetaData.set_file_pathFileMetaData.to_dictFileMetaData.write_metadata_fileFutureWarningGZIPINTINT32INT64INT96INTERVALINT_16INT_32INT_64INT_8Incompatible checksums (0x%x vs (0xc86835a, 0x6aaa2f8, 0x6e1a100) = (column_index, descending, nulls_first))IndexErrorInvalid sort key: {0}Invalid sort key direction: {0}Invalid value for coerce_timestamps: {0}JSONKeyErrorLISTLZ4LZOMAPMAP_KEY_VALUE_MAX_ROW_GROUP_SIZEMemoryErrorNONENativeFileNo value specified for struct attribute 'column_idx'No value specified for struct attribute 'descending'No value specified for struct attribute 'nulls_first'NotImplementedNotImplementedErrorPLAINPLAIN_DICTIONARY<ParquetColumnSchema>
  name: {0}
  path: {1}
  max_definition_level: {2}
  max_repetition_level: {3}
  physical_type: {4}
  logical_type: {5}
  converted_type (legacy): {6}ParquetLogicalTypeParquetLogicalType.__reduce_cython__ParquetLogicalType.__setstate_cython__ParquetLogicalType.to_jsonParquetReaderParquetReader.__reduce_cython__ParquetReader.__setstate_cython__ParquetReader.closeParquetReader.column_name_idxParquetReader.iter_batchesParquetReader.openParquetReader.read_allParquetReader.read_columnParquetReader.read_row_groupParquetReader.read_row_groupsParquetReader.scan_contentsParquetReader.set_batch_sizeParquetReader.set_use_threadsParquetSchemaParquetSchema.__reduce__ParquetSchema.columnParquetSchema.equalsParquetSchema.to_arrow_schemaParquetWriterParquetWriter.__reduce_cython__ParquetWriter.__setstate_cython__ParquetWriter.add_key_value_metadataParquetWriter.closeParquetWriter.write_tableParquet format '2.0' pseudo version is deprecated, use '2.4' or '2.6' for fine-grained feature selectionPickleError__Pyx_CFunc_7pyarrow_8_parquet_object__lParenParquetEncoding__rParen_to_py_9encoding_.<locals>.wrapRLERLE_DICTIONARYRowGroupMetaDataRowGroupMetaData.__reduce__RowGroupMetaData.columnRowGroupMetaData.equalsRowGroupMetaData.to_dictRow group size cannot be 0RuntimeErrorSNAPPYSTRINGSequenceSort key name '{0}' not found in schema:
{1}SortingColumnSortingColumn.__reduce_cython__SortingColumn.__setstate_cython__SortingColumn.from_orderingSortingColumn.to_dictSortingColumn.to_orderingSorting columns have inconsistent null placementStatisticsStatistics.__reduce_cython__Statistics.__setstate_cython__Statistics.equalsStatistics.to_dictTIMETIMESTAMPTIMESTAMP_MICROSTIMESTAMP_MILLISTIME_MICROSTIME_MILLISTime zones are not available from the C-API.To use 'column_encoding' set 'use_dictionary' to FalseTypeErrorUINT_16UINT_32UINT_64UINT_8UNCOMPRESSEDUNDEFINEDUNKNOWNUTF8UUIDUnrecognized file version, assuming 2.6: {}Unsupported Parquet data page version: {0}Unsupported Parquet format version: {0}Unsupported Writer Engine Version: {0}Unsupported column encoding: Unsupported compression: V1V1 parquet writer engine is a no-op.  Use V2.V2ValueErrorZSTD?.*{}
  {}    add_key_value_metadataallow_truncated_timestampsappend_row_groupsargsarrow_propsas_pyascendingasyncio.coroutinesat_endat_startbatch_sizebufferbuffer_sizebuilderc_batch_sizec_column_indicesc_metadatac_num_rowsc_pathc_row_group_sizec_row_groupsc_sinkc_wherecfunc.to_pychunk__class____class_getitem__cline_in_tracebackcloseclosedclscoerce_int96_timestamp_unitcoerce_timestampscolcol_bytescol_mapcollections.abccolumncolumn_encoding'column_encoding' should be a dictionary or a stringcolumn_idxcolumn_index{}(column_index={}, descending={}, nulls_first={})column_indicescolumn_namecolumn_name_idxcolumnscompressioncompression_levelcontainer_convert_sorting_columns.<locals>.genexprconverted_typecreated_byctableddata_page_offsetdata_page_sizedata_page_versiondecryption_propertiesdescending__dict__dict_dictdictionary_page_offsetdictionary_pagesize_limitdisabledistinct_countenableencoding_encodingsencryption_propertiesequalsfile metadata is only available after writer closefile_offsetfile_pathformatformat_versionfrom_orderingfrombytesgcgenexprget__getstate__getvaluehas_dictionary_pagehas_distinct_counthas_min_max{}
  has_min_max: {}
  min: {}
  max: {}
  null_count: {}
  distinct_count: {}
  num_values: {}
  physical_type: {}
  logical_type: {}
  converted_type (legacy): {}has_null_counti__import__indentindex_initializing is already used by default._is_coroutineis_stats_setis_threading_enabledisenableditemsiter_batchesiteritemskey_value_metadatalengthlogical_type__main__mapmaxmax_definition_levelmax_repetition_levelmemory_poolmetadatametadata_lenminms__name__name__new__no default __reduce__ due to non-trivial __cinit__not supported in parquet-cppnull_countnull_placementnull_placement must be "at_start" or "at_end"nulls_firstnum_columnsnum_row_groupsnum_rowsnum_valuesobjectopenorderotheroutpage_checksum_verificationparentparquet-cpp doesn't return valid valuespathpath_in_schemaphysical_typepicklepre_bufferprecisionpropertiespseudo-2.0pyarrow._parquetpyarrow/_parquet.pyxpyarrow.lib__pyx_PickleError__pyx_checksum__pyx_result__pyx_state__pyx_type__pyx_unpickle_SortingColumn__pyx_vtable__rangeread_allread_columnread_dictionaryread_row_groupread_row_groups_reconstruct_filemetadatarecord_batchrecordbatchreader__reduce____reduce_cython____reduce_ex____repr__resultrow_grouprow_group_sizerow_groupssafescalescan_contentsschemaselfsendserializedserialized_sizeset_batch_sizeset_file_pathset_use_threads__setstate____setstate_cython__sinksort_keysort_keyssorting_columnsorting_columns'sorting_columns' must be a list of `SortingColumn`sourcesp_arrow_schema__spec__stacklevelstatestaticmethodstatisticsstore_decimal_as_integer'store_decimal_as_integer' must be a booleanstore_schema_stringify_path<stringsource>table__test__textwrapthrift_container_size_limitthrift_container_size_limit must be larger than zerothrift_string_size_limitthrift_string_size_limit must be larger than zerothrowto_arrow_schemato_dictto_jsonto_orderingtobytestotal_byte_sizetotal_compressed_sizetotal_uncompressed_sizeupdateupperususe_byte_stream_split'use_byte_stream_split' cannot be passedtogether with 'column_encoding'use_compliant_nested_typeuse_deprecated_int96_timestampsuse_dictionaryuse_memory_mapuse_setstateuse_threadsversionwarnwarningswherewrapwrite_batch_sizewrite_metadata_filewrite_page_checksumwrite_page_indexwrite_statisticswrite_tablewriter_engine_versionparquet-cpp-arrow version 18.0.0-SNAPSHOT__pyx_unpickle_SortingColumn(__pyx_type, long __pyx_checksum, __pyx_state)ParquetWriter.__setstate_cython__(self, __pyx_state)ParquetWriter.__reduce_cython__(self)ParquetWriter.add_key_value_metadata(self, key_value_metadata)ParquetWriter.write_table(self, Table table, row_group_size=None)ParquetWriter.close(self)ParquetReader.__setstate_cython__(self, __pyx_state)ParquetReader.__reduce_cython__(self)ParquetReader.close(self)ParquetReader.read_column(self, int column_index)

        Read the column at the specified index.

        Parameters
        ----------
        column_index : int
            Index of the column.

        Returns
        -------
        column : pyarrow.ChunkedArray
        ParquetReader.column_name_idx(self, column_name)

        Find the index of a column by its name.

        Parameters
        ----------
        column_name : str
            Name of the column; separation of nesting levels is done via ".".

        Returns
        -------
        column_idx : int
            Integer index of the column in the schema.
        ParquetReader.scan_contents(self, column_indices=None, batch_size=65536)

        Parameters
        ----------
        column_indices : list[int], optional
        batch_size : int32, default 65536

        Returns
        -------
        num_rows : int64
        ParquetReader.read_all(self, column_indices=None, bool use_threads=True)

        Parameters
        ----------
        column_indices : list[int], optional
        use_threads : bool, default True

        Returns
        -------
        table : pyarrow.Table
        ParquetReader.read_row_groups(self, row_groups, column_indices=None, bool use_threads=True)

        Parameters
        ----------
        row_groups : list[int]
        column_indices : list[int], optional
        use_threads : bool, default True

        Returns
        -------
        table : pyarrow.Table
        ParquetReader.read_row_group(self, int i, column_indices=None, bool use_threads=True)

        Parameters
        ----------
        i : int
        column_indices : list[int], optional
        use_threads : bool, default True

        Returns
        -------
        table : pyarrow.Table
        ParquetReader.iter_batches(self, int64_t batch_size, row_groups, column_indices=None, bool use_threads=True)

        Parameters
        ----------
        batch_size : int64
        row_groups : list[int]
        column_indices : list[int], optional
        use_threads : bool, default True

        Yields
        ------
        next : RecordBatch
        ParquetReader.set_batch_size(self, int64_t batch_size)

        Parameters
        ----------
        batch_size : int64
        ParquetReader.set_use_threads(self, bool use_threads)

        Parameters
        ----------
        use_threads : bool
        ParquetReader.open(self, source, *, bool use_memory_map=False, read_dictionary=None, FileMetaData metadata=None, int buffer_size=0, bool pre_buffer=False, coerce_int96_timestamp_unit=None, FileDecryptionProperties decryption_properties=None, thrift_string_size_limit=None, thrift_container_size_limit=None, page_checksum_verification=False)

        Open a parquet file for reading.

        Parameters
        ----------
        source : str, pathlib.Path, pyarrow.NativeFile, or file-like object
        use_memory_map : bool, default False
        read_dictionary : iterable[int or str], optional
        metadata : FileMetaData, optional
        buffer_size : int, default 0
        pre_buffer : bool, default False
        coerce_int96_timestamp_unit : str, optional
        decryption_properties : FileDecryptionProperties, optional
        thrift_string_size_limit : int, optional
        thrift_container_size_limit : int, optional
        page_checksum_verification : bool, default False
        ColumnSchema.equals(self, ColumnSchema other)

        Return whether the two column schemas are equal.

        Parameters
        ----------
        other : ColumnSchema
            Schema to compare against.

        Returns
        -------
        are_equal : bool
        ColumnSchema.__reduce__(self)ParquetSchema.column(self, i)

        Return the schema for a single column.

        Parameters
        ----------
        i : int
            Index of column in schema.

        Returns
        -------
        column_schema : ColumnSchema
        ParquetSchema.equals(self, ParquetSchema other)

        Return whether the two schemas are equal.

        Parameters
        ----------
        other : ParquetSchema
            Schema to compare against.

        Returns
        -------
        are_equal : bool
        ParquetSchema.to_arrow_schema(self)

        Convert Parquet schema to effective Arrow schema.

        Returns
        -------
        schema : Schema
        ParquetSchema.__reduce__(self)FileMetaData.write_metadata_file(self, where)

        Write the metadata to a metadata-only Parquet file.

        Parameters
        ----------
        where : path or file-like object
            Where to write the metadata.  Should be a writable path on
            the local filesystem, or a writable file-like object.
        FileMetaData.append_row_groups(self, FileMetaData other)

        Append row groups from other FileMetaData object.

        Parameters
        ----------
        other : FileMetaData
            Other metadata to append row groups from.
        FileMetaData.set_file_path(self, path)

        Set ColumnChunk file paths to the given value.

        This method modifies the ``file_path`` field of each ColumnChunk
        in the FileMetaData to be a particular value.

        Parameters
        ----------
        path : str
            The file path to set on all ColumnChunks.
        FileMetaData.row_group(self, int i)

        Get metadata for row group at index i.

        Parameters
        ----------
        i : int
            Row group index to get.

        Returns
        -------
        row_group_metadata : RowGroupMetaData
        FileMetaData.equals(self, FileMetaData other)

        Return whether the two file metadata objects are equal.

        Parameters
        ----------
        other : FileMetaData
            Metadata to compare against.

        Returns
        -------
        are_equal : bool
        FileMetaData.to_dict(self)

        Get dictionary representation of the file metadata.

        Returns
        -------
        dict
            Dictionary with a key for each attribute of this class.
        FileMetaData.__reduce__(self)_reconstruct_filemetadata(Buffer serialized)RowGroupMetaData.to_dict(self)

        Get dictionary representation of the row group metadata.

        Returns
        -------
        dict
            Dictionary with a key for each attribute of this class.
        RowGroupMetaData.column(self, int i)

        Get column metadata at given index.

        Parameters
        ----------
        i : int
            Index of column to get metadata for.

        Returns
        -------
        ColumnChunkMetaData
            Metadata for column within this chunk.
        RowGroupMetaData.equals(self, RowGroupMetaData other)

        Return whether the two row group metadata objects are equal.

        Parameters
        ----------
        other : RowGroupMetaData
            Metadata to compare against.

        Returns
        -------
        are_equal : bool
        RowGroupMetaData.__reduce__(self)SortingColumn.__setstate_cython__(self, __pyx_state)SortingColumn.__reduce_cython__(self)SortingColumn.to_dict(self)

        Get dictionary representation of the SortingColumn.

        Returns
        -------
        dict
            Dictionary with a key for each attribute of this class.
        SortingColumn.to_ordering(Schema schema, sorting_columns)

        Convert a tuple of SortingColumn objects to the same format as
        :class:`pyarrow.compute.SortOptions`.

        Parameters
        ----------
        schema : Schema
            Schema of the input data.
        sorting_columns : tuple of SortingColumn
            Columns to sort the input on.

        Returns
        -------
        sort_keys : tuple of (name, order) tuples
        null_placement : {'at_start', 'at_end'}
        SortingColumn.from_ordering(cls, Schema schema, sort_keys, null_placement=u'at_end')

        Create a tuple of SortingColumn objects from the same arguments as
        :class:`pyarrow.compute.SortOptions`.

        Parameters
        ----------
        schema : Schema
            Schema of the input data.
        sort_keys : Sequence of (name, order) tuples
            Names of field/column keys (str) to sort the input on,
            along with the order each field/column is sorted in.
            Accepted values for `order` are "ascending", "descending".
        null_placement : {'at_start', 'at_end'}, default 'at_end'
            Where null values should appear in the sort order.

        Returns
        -------
        sorting_columns : tuple of SortingColumn
        ColumnChunkMetaData.__setstate_cython__(self, __pyx_state)ColumnChunkMetaData.__reduce_cython__(self)ColumnChunkMetaData.equals(self, ColumnChunkMetaData other)

        Return whether the two column chunk metadata objects are equal.

        Parameters
        ----------
        other : ColumnChunkMetaData
            Metadata to compare against.

        Returns
        -------
        are_equal : bool
        ColumnChunkMetaData.to_dict(self)

        Get dictionary representation of the column chunk metadata.

        Returns
        -------
        dict
            Dictionary with a key for each attribute of this class.
        ParquetLogicalType.__setstate_cython__(self, __pyx_state)ParquetLogicalType.__reduce_cython__(self)ParquetLogicalType.to_json(self)

        Get a JSON string containing type and type parameters.

        Returns
        -------
        json : str
            JSON representation of type, with at least a field called 'Type'
            which contains the type name. If the type is parameterized, such
            as a decimal with scale and precision, will contain those as fields
            as well.
        Statistics.__setstate_cython__(self, __pyx_state)Statistics.__reduce_cython__(self)Statistics.equals(self, Statistics other)

        Return whether the two column statistics objects are equal.

        Parameters
        ----------
        other : Statistics
            Statistics to compare against.

        Returns
        -------
        are_equal : bool
        Statistics.to_dict(self)

        Get dictionary representation of statistics.

        Returns
        -------
        dict
            Dictionary with a key for each attribute of this class.
        wrap(encoding_: 'ParquetEncoding')vector::_M_realloc_insertSt11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EESt19_Sp_make_shared_tagSt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EEN5arrow4util12CodecOptionsEN7parquet16ParquetExceptionEN7parquet16WriterProperties7BuilderESt23_Sp_counted_ptr_inplaceIN5arrow4util12CodecOptionsESaIvELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7parquet16WriterPropertiesELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7parquet21ArrowWriterPropertiesELN9__gnu_cxx12_Lock_policyE2EE
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