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duality-group / pyarrow python

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pyarrow / _dataset.cpython-310-aarch64-linux-gnu.so
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%.200spyarrow/_dataset.pyxpyarrow._dataset.WrittenFile.__init__pyarrow/_dataset.pxdpyarrow._dataset.WrittenFile.path.__set__pyarrow._dataset.PartitioningFactory.wrappyarrow._dataset.DatasetFactory.wrappyarrow._dataset.Scanner.wrappyarrow._dataset.Scanner.to_batches._iteratorstringsourcestring.to_py.__pyx_convert_PyBytes_string_to_py_std__in_stringpyarrow._dataset.WrittenFile.size.__get__pyarrow._dataset.Scanner.projected_schema.__get__pyarrow._dataset.Scanner.dataset_schema.__get__pyarrow._dataset.DatasetFactory.root_partition.__get__pyarrow._dataset.Partitioning.schema.__get__pyarrow._dataset.CsvFileFormat.parse_options.__get__pyarrow._dataset.Fragment.partition_expression.__get__pyarrow._dataset.FileSystemDataset.filesystem.__get__pyarrow._dataset.Dataset.schema.__get__pyarrow._dataset.Dataset.partition_expression.__get__name '%U' is not definedcannot import name %SMissing type objectCannot convert %.200s to %.200spyarrow._dataset.FileSystemFactoryOptions.exclude_invalid_files.__set____del__Argument '%.200s' has incorrect type (expected %.200s, got %.200s)othergenerator already executinggenerator ignored GeneratorExitpyarrow._dataset.CsvFileFormat.__reduce__pyarrow._dataset.UnionDataset.__reduce__pyarrow._dataset.CsvFragmentScanOptions.__reduce__parse_optionspyarrow._dataset.CsvFileFormat.__repr__pyarrow._dataset.InMemoryDataset.__setstate_cython__pyarrow._dataset.InMemoryDataset.__reduce_cython__pyarrow._dataset.Dataset.__setstate_cython__pyarrow._dataset.Fragment.__setstate_cython__pyarrow._dataset.Dataset.__reduce_cython__pyarrow._dataset.KeyValuePartitioning.__setstate_cython__pyarrow._dataset.DirectoryPartitioning.__setstate_cython__pyarrow._dataset.DirectoryPartitioning.__reduce_cython__pyarrow._dataset.Scanner.__reduce_cython__pyarrow._dataset.FileFormat.__reduce_cython__pyarrow._dataset.FileWriteOptions.__setstate_cython__pyarrow._dataset.FileWriteOptions.__reduce_cython__pyarrow._dataset.FileFormat.__setstate_cython__pyarrow._dataset.Fragment.__reduce_cython__pyarrow._dataset.IpcFileWriteOptions.__setstate_cython__pyarrow._dataset.IpcFileWriteOptions.__reduce_cython__pyarrow._dataset.FragmentScanOptions.__setstate_cython__pyarrow._dataset.FragmentScanOptions.__reduce_cython__pyarrow._dataset.Partitioning.__reduce_cython__pyarrow._dataset.CsvFileWriteOptions.__setstate_cython__pyarrow._dataset.CsvFileWriteOptions.__reduce_cython__pyarrow._dataset.TaggedRecordBatchIterator.__setstate_cython__pyarrow._dataset.TaggedRecordBatchIterator.__reduce_cython__pyarrow._dataset.RecordBatchIterator.__setstate_cython__pyarrow._dataset.RecordBatchIterator.__reduce_cython__pyarrow._dataset.UnionDatasetFactory.__setstate_cython__pyarrow._dataset.UnionDatasetFactory.__reduce_cython__pyarrow._dataset.FileSystemDatasetFactory.__setstate_cython__pyarrow._dataset.FileSystemDatasetFactory.__reduce_cython__pyarrow._dataset.FileSystemFactoryOptions.__setstate_cython__pyarrow._dataset.FileSystemFactoryOptions.__reduce_cython__pyarrow._dataset.DatasetFactory.__setstate_cython__pyarrow._dataset.DatasetFactory.__reduce_cython__pyarrow._dataset.FilenamePartitioning.__setstate_cython__pyarrow._dataset.FilenamePartitioning.__reduce_cython__pyarrow._dataset.HivePartitioning.__setstate_cython__pyarrow._dataset.HivePartitioning.__reduce_cython__pyarrow._dataset.KeyValuePartitioning.__reduce_cython__pyarrow._dataset.PartitioningFactory.__setstate_cython__pyarrow._dataset.PartitioningFactory.__reduce_cython__pyarrow._dataset.Partitioning.__setstate_cython__pyarrow._dataset.Scanner.__setstate_cython___iteratorpyarrow._dataset._get_segment_encodingpyarrow._dataset.FileFormat._set_default_fragment_scan_optionspyarrow._dataset._get_orc_fileformatpyarrow._dataset._get_parquet_classespyarrow._dataset.WrittenFile.__reduce_cython__Unable to initialize pickling for %sModule '_dataset' has already been imported. Re-initialisation is not supported.%d.%dpyarrow._datasetcompiletime version %s of module '%.100s' does not match runtime version %scython_runtime_Weakrefable_true%.200s does not export expected C variable %.200sarrow::compute::ExpressionC variable %.200s.%.200s has wrong signature (expected %.500s, got %.500s)int (arrow::Status const &)check_status arrow::MemoryPool *(struct __pyx_obj_7pyarrow_3lib_MemoryPool *)maybe_unbox_memory_poolstruct __pyx_obj_7pyarrow_3lib_NativeFile *(PyObject *, bool)get_native_filePyObject *(std::shared_ptr< arrow::Buffer>  const &)pyarrow_wrap_bufferPyObject *(std::shared_ptr< arrow::Schema>  const &)pyarrow_wrap_schemaPyObject *(std::shared_ptr< arrow::Scalar>  const &)pyarrow_wrap_scalarPyObject *(std::shared_ptr< arrow::Array>  const &)pyarrow_wrap_arrayPyObject *(std::shared_ptr< arrow::RecordBatch>  const &)pyarrow_wrap_batchPyObject *(std::shared_ptr< arrow::Table>  const &)pyarrow_wrap_tablestd::shared_ptr< arrow::Buffer>  (PyObject *)pyarrow_unwrap_bufferstd::shared_ptr< arrow::Schema>  (PyObject *)pyarrow_unwrap_schemastd::shared_ptr< arrow::Array>  (PyObject *)pyarrow_unwrap_arraystd::shared_ptr< arrow::Table>  (PyObject *)pyarrow_unwrap_tablearrow::compute::Expression (struct __pyx_obj_7pyarrow_8_compute_Expression *, struct __pyx_obj_7pyarrow_3lib_Schema *)_bindcollections.abcCython module failed to register with collections.abc modulebackports_abcmetaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its basesinit pyarrow._datasetUninitialized Result<T>at mostat leastpyarrow._dataset.FileSystemFactoryOptions.__init__selector_ignore_prefixespyarrow._dataset.FileSystemFactoryOptions.selector_ignore_prefixes.__get__takepyarrow._dataset.Fragment.takepyarrow._dataset.Dataset.takepyarrow._dataset.CsvFileFormat.equalspyarrow._dataset.Scanner.to_batchespyarrow._dataset.FileSystemDataset.__reduce__joinpyarrow._dataset.Dataset.joinpyarrow._dataset._get_parquet_symbolstring.from_py.__pyx_convert_string_from_py_std__in_stringscannerpyarrow._dataset.Dataset.scannerpyarrow._dataset.CsvFragmentScanOptions.equalspyarrow._dataset.RecordBatchIterator.__init__pyarrow._dataset.TaggedRecordBatchIterator.__init__raise: arg 3 must be a traceback or Noneinstance exception may not have a separate valuethrowget_fragmentspyarrow._dataset.Dataset.get_fragmentsfilterto_batchespyarrow._dataset.Dataset.to_batchescount_rowspyarrow._dataset.Dataset.count_rowspyarrow._dataset.Fragment.count_rowsto_tablepyarrow._dataset.Dataset.to_tablepyarrow._dataset.FileSystemFactoryOptions.partition_base_dir.__set__read_optionspyarrow._dataset.Fragment.wrappyarrow._dataset.FileWriteOptions.wrapunbound method %.200S() needs an argument%.200s() takes no arguments (%zd given)%.200s() takes exactly one argument (%zd given)Bad call flags in __Pyx_CyFunction_Call. METH_OLDARGS is no longer supported!%.200s() takes no keyword argumentspyarrow._dataset.WrittenFile.size.__set__make_write_optionspyarrow._dataset.CsvFileFormat.make_write_optionspyarrow._dataset.FileSystemFactoryOptions.partition_base_dir.__get__pyarrow._dataset.IpcFileFormat.__reduce__pyarrow._dataset.FragmentScanOptions.__eq__pyarrow._dataset.FileFormat.__eq__pyarrow._dataset.PartitioningFactory.type_name.__get__pyarrow._dataset.FragmentScanOptions.type_name.__get__pyarrow._dataset.FileFormat.default_extname.__get__pyarrow._dataset.FileFormat.wrappyarrow._dataset._filesystemdataset_write_visitor'NoneType' object is not subscriptablepyarrow._dataset.FileFragment.format.__get__pyarrow._dataset.FileWriteOptions.format.__get__pyarrow._dataset.FileSystemDataset.format.__get__from_pathstoo many values to unpack (expected %zd)need more than %zd value%.1s to unpack'NoneType' object is not iterable'%.200s' object is not subscriptablecannot fit '%.200s' into an index-sized integerpyarrow._dataset.FileSystemDataset.from_pathspyarrow._dataset.CsvFileWriteOptions.__init__pyarrow._dataset.FragmentScanOptions.__init__pyarrow._dataset.IpcFileWriteOptions.__init__pyarrow._dataset.DatasetFactory.__init__pyarrow._dataset.KeyValuePartitioning.__init__pyarrow._dataset.Dataset.__init__pyarrow._dataset.FileFormat.__init__pyarrow._dataset.PartitioningFactory.__init__pyarrow._dataset.Partitioning.__init__pyarrow._dataset.Scanner.__init__pyarrow._dataset.FileWriteOptions.__init__pyarrow._dataset.Fragment.__init__headpyarrow._dataset.Fragment.headpyarrow._dataset.Dataset.headpyarrow._dataset.Dataset.wrap_forbid_instantiationpyarrow._dataset._forbid_instantiationpyarrow._dataset.Partitioning.wrappyarrow._dataset.FileFragment.__reduce__hasattr(): attribute name must be stringpyarrow._dataset.__pyx_unpickle_WrittenFile__set_statetuplepyarrow._dataset.WrittenFile.__setstate_cython____pyx_unpickle_WrittenFilepyarrow._dataset.__pyx_unpickle_WrittenFiledictionary changed size during iterationpyarrow._dataset.FileFragment.path.__get__pyarrow._dataset.FragmentScanOptions.wrappyarrow._dataset.FileFormat.default_fragment_scan_options.__get__'%.50s' object has no attribute '%U'functionpyarrow._dataset.Fragment.to_tablepyarrow._dataset.Fragment.to_batchespyarrow._dataset.Fragment.scanneritems'NoneType' object has no attribute '%.30s'pyarrow._dataset.FileFragment.__repr__vector.from_py.__pyx_convert_vector_from_py_std_3a__3a_stringvector::_M_realloc_insertpyarrow._dataset.FileSystemFactoryOptions.selector_ignore_prefixes.__set__pyarrow._dataset.CsvFragmentScanOptions.read_options.__get__pyarrow._dataset.FileSystemDataset.files.__get__pyarrow._dataset.UnionDataset.children.__get__pyarrow._dataset.CsvFileFormat._set_default_fragment_scan_optionspyarrow._dataset.FileSystemFactoryOptions.partitioning_factory.__get__write_optionspyarrow._dataset.FileSystemFactoryOptions.partitioning.__get__pyarrow._dataset._make_file_sourcepyarrow._dataset.FileFormat.default_fragment_scan_options.__set__pyarrow._dataset.CsvFragmentScanOptions.__init__convert_optionspyarrow._dataset.CsvFileFormat.__init__argument after ** must be a mapping, not NoneTypepyarrow._dataset.IpcFileFormat.__init__discoverpyarrow._dataset.FilenamePartitioning.discoverpyarrow._dataset.DirectoryPartitioning.discoverpyarrow._dataset.InMemoryDataset.__init__exprpyarrow._dataset.DatasetFactory.root_partition.__set__pyarrow._dataset.KeyValuePartitioning.dictionaries.__get__pyarrow._dataset.FileFragment.buffer.__get__pyarrow._dataset.FileFragment.filesystem.__get__pyarrow._dataset.FileFormat.make_write_optionspyarrow._dataset.CsvFileWriteOptions.write_options.__get__pyarrow._dataset.HivePartitioning.discoverpyarrow._dataset.Scanner.count_rowspyarrow._dataset.FileFormat._finish_writepyarrow._dataset.Fragment.physical_schema.__get__pyarrow._dataset.DatasetFactory.inspectpyarrow._dataset.Scanner.to_tablepyarrow._dataset.Scanner.headvalue_filesystemdataset_writepyarrow._dataset._filesystemdataset_writedatabase_dirArgument '%.200s' must not be Nonebasename_templatefilesystempartitioningfile_optionsexisting_data_behaviorpyarrow._dataset.FileFormat.make_fragmentmake_fragmentpartition_expressionpyarrow._dataset.FileFormat.inspectpyarrow._dataset.TaggedRecordBatchIterator.__next__pyarrow._dataset.Partitioning.parsepyarrow._dataset.DatasetFactory.inspect_schemaspyarrow._dataset.FileSystemDataset.partitioning.__get__pyarrow._dataset.Scanner.takepyarrow._dataset.DatasetFactory.finishpyarrow._dataset.TaggedRecordBatchIterator.wrappyarrow._dataset.RecordBatchIterator.wrappyarrow._dataset.Dataset.replace_schemapyarrow._dataset.RecordBatchIterator.__next__pyarrow._dataset.Scanner.to_readerpyarrow._dataset.FileSystemDatasetFactory.__init__formatpyarrow._dataset.Scanner.scan_batchespyarrow._dataset.CsvFragmentScanOptions.convert_options.__get__pyarrow._dataset.UnionDataset.__init__pyarrow._dataset.FileSystemDataset.__init__pyarrow._dataset._partitioning_dictionariespyarrow._dataset.FilenamePartitioning.__init__pyarrow._dataset.HivePartitioning.__init__pyarrow._dataset.DirectoryPartitioning.__init__pyarrow._dataset.UnionDatasetFactory.__init__factoriespyarrow._dataset._populate_builderfrom_datasetpyarrow._dataset.Scanner.from_datasetdatasetmemory_poolfrom_fragmentpyarrow._dataset.Scanner.from_fragmentfragmentfrom_batchespyarrow._dataset.Scanner.from_batchespyarrow._dataset._get_partition_keysgenerator__name__name of the generator__qualname__qualified name of the generatorgi_frameFrame of the generatorgi_runninggi_yieldfromobject being iterated by 'yield from', or Nonegi_codesendsend(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.closeclose() -> raise GeneratorExit inside generator.cython_function_or_method__module__func_doc__doc__func_name__self__func_dict__dict__func_globals__globals__func_closure__closure__func_code__code__func_defaults__defaults____kwdefaults____annotations__pyarrow._dataset.__pyx_scope_struct_1__iteratorpyarrow._dataset.__pyx_scope_struct__get_fragmentspyarrow._dataset.ScannerScanner()
A materialized scan operation with context and options bound.

    A scanner is the class that glues the scan tasks, data fragments and data
    sources together.

    Parameters
    ----------
    dataset : Dataset
        Dataset to scan.
    columns : list of str or dict, default None
        The columns to project. This can be a list of column names to
        include (order and duplicates will be preserved), or a dictionary
        with {{new_column_name: expression}} values for more advanced
        projections.

        The list of columns or expressions may use the special fields
        `__batch_index` (the index of the batch within the fragment), 
        `__fragment_index` (the index of the fragment within the dataset), 
        `__last_in_fragment` (whether the batch is last in fragment), and
        `__filename` (the name of the source file or a description of the 
        source fragment).

        The columns will be passed down to Datasets and corresponding data
        fragments to avoid loading, copying, and deserializing columns
        that will not be required further down the compute chain.
        By default all of the available columns are projected. 
        Raises an exception if any of the referenced column names does 
        not exist in the dataset's Schema.
    filter : Expression, default None
        Scan will return only the rows matching the filter.
        If possible the predicate will be pushed down to exploit the
        partition information or internal metadata found in the data
        source, e.g. Parquet statistics. Otherwise filters the loaded
        RecordBatches before yielding them.
    batch_size : int, default 128Ki
        The maximum row count for scanned record batches. If scanned
        record batches are overflowing memory then this method can be
        called to reduce their size.
    use_threads : bool, default True
        If enabled, then maximum parallelism will be used determined by
        the number of available CPU cores.
    use_async : bool, default True
        This flag is deprecated and is being kept for this release for
        backwards compatibility.  It will be removed in the next release.
    memory_pool : MemoryPool, default None
        For memory allocations, if required. If not specified, uses the
        default pool.
    dataset_schemaThe schema with which batches will be read from fragments.projected_schema
        The materialized schema of the data, accounting for projections.

        This is the schema of any data returned from the scanner.
        scan_batchesto_readerpyarrow._dataset.TaggedRecordBatchIteratorTaggedRecordBatchIterator()
An iterator over a sequence of record batches with fragments.__next__pyarrow._dataset.RecordBatchIteratorRecordBatchIterator()
An iterator over a sequence of record batches.pyarrow._dataset.UnionDatasetFactoryUnionDatasetFactory(list factories)

    Provides a way to inspect/discover a Dataset's expected schema before
    materialization.

    Parameters
    ----------
    factories : list of DatasetFactory
    pyarrow._dataset.FileSystemDatasetFactoryFileSystemDatasetFactory(FileSystem filesystem, paths_or_selector, FileFormat format, FileSystemFactoryOptions options=None)

    Create a DatasetFactory from a list of paths with schema inspection.

    Parameters
    ----------
    filesystem : pyarrow.fs.FileSystem
        Filesystem to discover.
    paths_or_selector : pyarrow.fs.FileSelector or list of path-likes
        Either a Selector object or a list of path-like objects.
    format : FileFormat
        Currently only ParquetFileFormat and IpcFileFormat are supported.
    options : FileSystemFactoryOptions, optional
        Various flags influencing the discovery of filesystem paths.
    pyarrow._dataset.FileSystemFactoryOptionsFileSystemFactoryOptions(partition_base_dir=None, partitioning=None, exclude_invalid_files=None, list selector_ignore_prefixes=None)

    Influences the discovery of filesystem paths.

    Parameters
    ----------
    partition_base_dir : str, optional
        For the purposes of applying the partitioning, paths will be
        stripped of the partition_base_dir. Files not matching the
        partition_base_dir prefix will be skipped for partitioning discovery.
        The ignored files will still be part of the Dataset, but will not
        have partition information.
    partitioning : Partitioning/PartitioningFactory, optional
       Apply the Partitioning to every discovered Fragment. See Partitioning or
       PartitioningFactory documentation.
    exclude_invalid_files : bool, optional (default True)
        If True, invalid files will be excluded (file format specific check).
        This will incur IO for each files in a serial and single threaded
        fashion. Disabling this feature will skip the IO, but unsupported
        files may be present in the Dataset (resulting in an error at scan
        time).
    selector_ignore_prefixes : list, optional
        When discovering from a Selector (and not from an explicit file list),
        ignore files and directories matching any of these prefixes.
        By default this is ['.', '_'].
    Partitioning to apply to discovered files.

        NOTE: setting this property will overwrite partitioning_factory.
        partitioning_factoryPartitioningFactory to apply to discovered files and
        discover a Partitioning.

        NOTE: setting this property will overwrite partitioning.
        partition_base_dir
        Base directory to strip paths before applying the partitioning.
        exclude_invalid_filesWhether to exclude invalid files.
        List of prefixes. Files matching one of those prefixes will be
        ignored by the discovery process.
        pyarrow._dataset.FilenamePartitioningFilenamePartitioning(Schema schema, dictionaries=None, segment_encoding=u'uri')

    A Partitioning based on a specified Schema.

    The FilenamePartitioning expects one segment in the file name for each
    field in the schema (all fields are required to be present) separated
    by '_'. For example given schema<year:int16, month:int8> the name
    ``"2009_11_"`` would be parsed to ("year" == 2009 and "month" == 11).

    Parameters
    ----------
    schema : Schema
        The schema that describes the partitions present in the file path.
    dictionaries : dict[str, Array]
        If the type of any field of `schema` is a dictionary type, the
        corresponding entry of `dictionaries` must be an array containing
        every value which may be taken by the corresponding column or an
        error will be raised in parsing.
    segment_encoding : str, default "uri"
        After splitting paths into segments, decode the segments. Valid
        values are "uri" (URI-decode segments) and "none" (leave as-is).

    Returns
    -------
    FilenamePartitioning

    Examples
    --------
    >>> from pyarrow.dataset import FilenamePartitioning
    >>> partitioning = FilenamePartitioning(
    ...     pa.schema([("year", pa.int16()), ("month", pa.int8())]))
    >>> print(partitioning.parse("2009_11_data.parquet"))
    ((year == 2009) and (month == 11))
    pyarrow._dataset.HivePartitioningHivePartitioning(Schema schema, dictionaries=None, null_fallback=u'__HIVE_DEFAULT_PARTITION__', segment_encoding=u'uri')

    A Partitioning for "/$key=$value/" nested directories as found in
    Apache Hive.

    Multi-level, directory based partitioning scheme originating from
    Apache Hive with all data files stored in the leaf directories. Data is
    partitioned by static values of a particular column in the schema.
    Partition keys are represented in the form $key=$value in directory names.
    Field order is ignored, as are missing or unrecognized field names.

    For example, given schema<year:int16, month:int8, day:int8>, a possible
    path would be "/year=2009/month=11/day=15".

    Parameters
    ----------
    schema : Schema
        The schema that describes the partitions present in the file path.
    dictionaries : dict[str, Array]
        If the type of any field of `schema` is a dictionary type, the
        corresponding entry of `dictionaries` must be an array containing
        every value which may be taken by the corresponding column or an
        error will be raised in parsing.
    null_fallback : str, default "__HIVE_DEFAULT_PARTITION__"
        If any field is None then this fallback will be used as a label
    segment_encoding : str, default "uri"
        After splitting paths into segments, decode the segments. Valid
        values are "uri" (URI-decode segments) and "none" (leave as-is).

    Returns
    -------
    HivePartitioning

    Examples
    --------
    >>> from pyarrow.dataset import HivePartitioning
    >>> partitioning = HivePartitioning(
    ...     pa.schema([("year", pa.int16()), ("month", pa.int8())]))
    >>> print(partitioning.parse("/year=2009/month=11/"))
    ((year == 2009) and (month == 11))

    pyarrow._dataset.DirectoryPartitioningDirectoryPartitioning(Schema schema, dictionaries=None, segment_encoding=u'uri')

    A Partitioning based on a specified Schema.

    The DirectoryPartitioning expects one segment in the file path for each
    field in the schema (all fields are required to be present).
    For example given schema<year:int16, month:int8> the path "/2009/11" would
    be parsed to ("year"_ == 2009 and "month"_ == 11).

    Parameters
    ----------
    schema : Schema
        The schema that describes the partitions present in the file path.
    dictionaries : dict[str, Array]
        If the type of any field of `schema` is a dictionary type, the
        corresponding entry of `dictionaries` must be an array containing
        every value which may be taken by the corresponding column or an
        error will be raised in parsing.
    segment_encoding : str, default "uri"
        After splitting paths into segments, decode the segments. Valid
        values are "uri" (URI-decode segments) and "none" (leave as-is).

    Returns
    -------
    DirectoryPartitioning

    Examples
    --------
    >>> from pyarrow.dataset import DirectoryPartitioning
    >>> partitioning = DirectoryPartitioning(
    ...     pa.schema([("year", pa.int16()), ("month", pa.int8())]))
    >>> print(partitioning.parse("/2009/11/"))
    ((year == 2009) and (month == 11))
    pyarrow._dataset.KeyValuePartitioningKeyValuePartitioning()dictionaries
        The unique values for each partition field, if available.

        Those values are only available if the Partitioning object was
        created through dataset discovery from a PartitioningFactory, or
        if the dictionaries were manually specified in the constructor.
        If no dictionary field is available, this returns an empty list.
        pyarrow._dataset.CsvFileWriteOptionsCsvFileWriteOptions()pyarrow._dataset.CsvFragmentScanOptionsCsvFragmentScanOptions(ConvertOptions convert_options=None, ReadOptions read_options=None)

    Scan-specific options for CSV fragments.

    Parameters
    ----------
    convert_options : pyarrow.csv.ConvertOptions
        Options regarding value conversion.
    read_options : pyarrow.csv.ReadOptions
        General read options.
    pyarrow._dataset.CsvFileFormatCsvFileFormat(ParseOptions parse_options=None, default_fragment_scan_options=None, ConvertOptions convert_options=None, ReadOptions read_options=None)

    FileFormat for CSV files.

    Parameters
    ----------
    parse_options : pyarrow.csv.ParseOptions
        Options regarding CSV parsing.
    convert_options : pyarrow.csv.ConvertOptions
        Options regarding value conversion.
    read_options : pyarrow.csv.ReadOptions
        General read options.
    default_fragment_scan_options : CsvFragmentScanOptions
        Default options for fragments scan.
    pyarrow._dataset.IpcFileFormatIpcFileFormat()default_extnamepyarrow._dataset.IpcFileWriteOptionsIpcFileWriteOptions()pyarrow._dataset.FileSystemDatasetFileSystemDataset(fragments, Schema schema, FileFormat format, FileSystem filesystem=None, root_partition=None)

    A Dataset of file fragments.

    A FileSystemDataset is composed of one or more FileFragment.

    Parameters
    ----------
    fragments : list[Fragments]
        List of fragments to consume.
    schema : Schema
        The top-level schema of the Dataset.
    format : FileFormat
        File format of the fragments, currently only ParquetFileFormat,
        IpcFileFormat, and CsvFileFormat are supported.
    filesystem : FileSystem
        FileSystem of the fragments.
    root_partition : Expression, optional
        The top-level partition of the DataDataset.
    
        The partitioning of the Dataset source, if discovered.

        If the FileSystemDataset is created using the ``dataset()`` factory
        function with a partitioning specified, this will return the
        finalized Partitioning object from the dataset discovery. In all
        other cases, this returns None.
        List of the filesThe FileFormat of this source.pyarrow._dataset.UnionDatasetUnionDataset(Schema schema, children)

    A Dataset wrapping child datasets.

    Children's schemas must agree with the provided schema.

    Parameters
    ----------
    schema : Schema
        A known schema to conform to.
    children : list of Dataset
        One or more input children
    childrenpyarrow._dataset.InMemoryDatasetInMemoryDataset(source, Schema schema=None)

    A Dataset wrapping in-memory data.

    Parameters
    ----------
    source : The data for this dataset.
        Can be a RecordBatch, Table, list of
        RecordBatch/Table, iterable of RecordBatch, or a RecordBatchReader.
        If an iterable is provided, the schema must also be provided.
    schema : Schema, optional
        Only required if passing an iterable as the source.
    pyarrow._dataset.WrittenFileWrittenFile(path, metadata, size)

    Metadata information about files written as
    part of a dataset write operation

    Parameters
    ----------
    path : str
        Path to the file.
    metadata : pyarrow.parquet.FileMetaData, optional
        For Parquet files, the Parquet file metadata.
    size : int
        The size of the file in bytes.
    pathpath: unicodemetadatametadata: objectsizesize: 'int'pyarrow._dataset.PartitioningFactoryPartitioningFactory()type_namepyarrow._dataset.PartitioningThe arrow Schema attached to the partitioning.parsepyarrow._dataset.FileFragmentA Fragment representing a data file.
        The path of the data file viewed by this fragment, if it views a
        file. If instead it views a buffer, this will be "<Buffer>".
        
        The FileSystem containing the data file viewed by this fragment, if
        it views a file. If instead it views a buffer, this will be None.
        buffer
        The buffer viewed by this fragment, if it views a buffer. If
        instead it views a file, this will be None.
        
        The format of the data file viewed by this fragment.
        pyarrow._dataset.FragmentFragment()
Fragment of data from a Dataset.physical_schemaReturn the physical schema of this Fragment. This schema can be
        different from the dataset read schema.An Expression which evaluates to true for all data viewed by this
        Fragment.
        pyarrow._dataset.FileWriteOptionsFileWriteOptions()pyarrow._dataset.FileFormatFileFormat()default_fragment_scan_optionspyarrow._dataset.FragmentScanOptionsFragmentScanOptions()
Scan options specific to a particular fragment and scan operation.pyarrow._dataset.DatasetDataset()

    Collection of data fragments and potentially child datasets.

    Arrow Datasets allow you to query against data that has been split across
    multiple files. This sharding of data may indicate partitioning, which
    can accelerate queries that only touch some partitions (files).
    
        An Expression which evaluates to true for all data viewed by this
        Dataset.
        The common schema of the full Datasetreplace_schemapyarrow._dataset.DatasetFactoryDatasetFactory()

    DatasetFactory is used to create a Dataset, inspect the Schema
    of the fragments contained in it, and declare a partitioning.
    root_partition_get_partition_keys_get_parquet_symbol_get_parquet_classes_get_orc_fileformatConstructed with a non-error status: Called Open() on an uninitialized FileSourcevector::_M_default_appendSt11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EEN5arrow4util18EqualityComparableINS_7dataset12PartitioningEEEFN5arrow6ResultISt10shared_ptrINS_2io16RandomAccessFileEEEEvESt19_Sp_make_shared_tagSt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EEN5arrow7dataset19FragmentScanOptionsEN5arrow7dataset12PartitioningEN5arrow7dataset22CsvFragmentScanOptionsEZN5arrow7dataset10FileSourceC4ESt10shared_ptrINS_2io16RandomAccessFileEENS_11Compression4typeEEUlvE_PFN5arrow6ResultISt10shared_ptrINS_2io16RandomAccessFileEEEEvEN5arrow7dataset29FileSystemDatasetWriteOptions17writer_pre_finishMUlPNS0_10FileWriterEE_EN5arrow7dataset29FileSystemDatasetWriteOptions18writer_post_finishMUlPNS0_10FileWriterEE_EZN5arrow2py12BindFunctionIFNS_6StatusEPNS_7dataset10FileWriterEEvJS5_EEESt8functionIT_EPFT0_P7_objectDpT1_ESC_EUlS5_E_St15_Sp_counted_ptrIPN5arrow7dataset13IpcFileFormatELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN5arrow7dataset13CsvFileFormatELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN5arrow7dataset22CsvFragmentScanOptionsELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN5arrow3csv12WriteOptionsELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN5arrow7dataset11ScanOptionsESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN5arrow7dataset15InMemoryDatasetESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN5arrow7dataset21DirectoryPartitioningESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN5arrow7dataset16HivePartitioningESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN5arrow7dataset20FilenamePartitioningESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN5arrow8IteratorISt10shared_ptrINS0_11RecordBatchEEEESaIS5_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN5arrow8IteratorINS0_7dataset17TaggedRecordBatchEEESaIS4_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN5arrow7dataset14ScannerBuilderESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN5arrow2py13BoundFunctionIFvP7_objectPNS0_7dataset10FileWriterEEEESaIS9_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN5arrow8IteratorISt10shared_ptrINS0_7dataset8FragmentEEEESaIS6_ELN9__gnu_cxx12_Lock_policyE2EE@@csv__HIVE_DEFAULT_PARTITION__cannot construct a FileSource from Must provide schema to construct scanner from an iterableself.wrapped cannot be converted to a Python object for picklingself.scanner,self.wrapped cannot be converted to a Python object for picklingself.partitioning,self.wrapped cannot be converted to a Python object for picklingself.options cannot be converted to a Python object for picklingself.iterator cannot be converted to a Python object for picklingself.hive_partitioning,self.keyvalue_partitioning,self.partitioning,self.wrapped cannot be converted to a Python object for picklingself.fragment,self.wrapped cannot be converted to a Python object for picklingself.factory,self.wrapped cannot be converted to a Python object for picklingself.factory,self.filesystem_factory,self.wrapped cannot be converted to a Python object for picklingself.directory_partitioning,self.keyvalue_partitioning,self.partitioning,self.wrapped cannot be converted to a Python object for picklingself.dataset,self.wrapped cannot be converted to a Python object for picklingself.dataset,self.in_memory_dataset,self.wrapped cannot be converted to a Python object for picklingself.c_options,self.wrapped cannot be converted to a Python object for picklingmax_partition_dictionary_size must be 0, -1, or Noneexisting_data_behavior must be one of 'error', cannot construct a FileSource from a path without a FileSystemThe use_async flag is deprecated and has no effect.  It will be removed in the next release.The number of files resulting from paths_or_selector must be equal to the number of partitions.Neither field_names nor schema was passed; cannot infer field_namesMust provide schema to construct in-memory dataset from an empty listMust pass either paths or a FileSelector, but passed {}Incompatible checksums (0x%x vs (0x14f8208, 0x2aeeab1, 0xb9a0db3) = (metadata, path, size))If `default_fragment_scan_options` is given, cannot specify convert_options or read_optionsExpected an Expression for a 'column' dictionary value, got {} insteadExpected a table, batch, or list of tables/batches instead of the given type: Expected a list or a dict for 'columns', got {} instead.Expected a list of tables or batches. The given list contains a Dataset is currently unstable. APIs subject to change without notice.Cannot specify a schema when providing a RecordBatchReaderCannot set fragment scan options for 'Argument 'root_partition' has incorrect type (expected Epression, got {0})Argument '{0}' has incorrect type (expected {1}, got {2})
which does not match expected schema
_set_default_fragment_scan_optionsself.keyvalue_partitioning,self.partitioning,self.wrapped cannot be converted to a Python object for picklingself.format,self.wrapped cannot be converted to a Python object for picklingself.filename_partitioning,self.keyvalue_partitioning,self.partitioning,self.wrapped cannot be converted to a Python object for picklingself.factory,self.union_factory,self.wrapped cannot be converted to a Python object for picklingself.c_options,self.csv_options,self.wrapped cannot be converted to a Python object for pickling'overwrite_or_ignore' or 'delete_matching'{} is an abstract class thus cannot be initialized.`default_fragment_scan_options` must be either a dictionary or an instance of CsvFragmentScanOptions Use one of the subclasses instead: {}Expected a RecordBatchReader or an iterable of batches instead of the given type: 
    A combination of a record batch and the fragment it came from.

    Parameters
    ----------
    record_batch : RecordBatch
        The record batch.
    fragment : Fragment
        Fragment of the record batch.
    
        Build a scan operation against the dataset.

        Data is not loaded immediately. Instead, this produces a Scanner,
        which exposes further operations (e.g. loading all data as a
        table, counting rows).

        See the `Scanner.from_dataset` method for further information.

        Parameters
        ----------
        **kwargs : dict, optional
            Arguments for `Scanner.from_dataset`.

        Returns
        -------
        scanner : Scanner

        Examples
        --------
        >>> import pyarrow as pa
        >>> table = pa.table({'year': [2020, 2022, 2021, 2022, 2019, 2021],
        ...                   'n_legs': [2, 2, 4, 4, 5, 100],
        ...                   'animal': ["Flamingo", "Parrot", "Dog", "Horse",
        ...                              "Brittle stars", "Centipede"]})
        >>> 
        >>> import pyarrow.parquet as pq
        >>> pq.write_table(table, "dataset_scanner.parquet")

        >>> import pyarrow.dataset as ds
        >>> dataset = ds.dataset("dataset_scanner.parquet")

        Selecting a subset of the columns:

        >>> dataset.scanner(columns=["year", "n_legs"]).to_table()
        pyarrow.Table
        year: int64
        n_legs: int64
        ----
        year: [[2020,2022,2021,2022,2019,2021]]
        n_legs: [[2,2,4,4,5,100]]

        Projecting selected columns using an expression:

        >>> dataset.scanner(columns={
        ...     "n_legs_uint": ds.field("n_legs").cast("uint8"),
        ... }).to_table()
        pyarrow.Table
        n_legs_uint: uint8
        ----
        n_legs_uint: [[2,2,4,4,5,100]]

        Filtering rows while scanning:

        >>> dataset.scanner(filter=ds.field("year") > 2020).to_table()
        pyarrow.Table
        year: int64
        n_legs: int64
        animal: string
        ----
        year: [[2022,2021,2022,2021]]
        n_legs: [[2,4,4,100]]
        animal: [["Parrot","Dog","Horse","Centipede"]]
        max_partition_dictionary_sizedefault_fragment_scan_options<pyarrow.dataset.{0}{1} path={2}{3}>to_batches.<locals>._iterator<CsvFileFormat parse_options=ParquetFragmentScanOptionsTaggedRecordBatchIteratorselector_ignore_prefixes__pyx_unpickle_WrittenFilepyarrow._dataset_parquetUnknown segment encoding: FileSystemFactoryOptionsFileSystemDatasetFactoryDataset.scanner (line 233)_filesystemdataset_writeParquetFileWriteOptionsexisting_data_behavior__HIVE_DEFAULT_PARTITION__CsvFragmentScanOptionsfragment_scan_optionsexclude_invalid_filesDirectoryPartitioningDataset.get_fragmentspyarrow/_dataset.pyxpyarrow._dataset_orcpartitioning_factorypartition_expression_forbid_instantiationKeyValuePartitioningFilenamePartitioningoverwrite_or_ignore_get_parquet_classesUnionDatasetFactoryRecordBatchIteratorPartitioningFactoryParquetFileFragmentNotImplementedErrorIpcFileWriteOptionsFragmentScanOptionsCsvFileWriteOptionssubclasses_insteadpartition_base_dirmin_rows_per_groupmax_rows_per_groupmake_write_options_get_partition_keys_get_parquet_symbol_get_orc_fileformatcline_in_traceback_DEFAULT_BATCH_SIZEpaths_or_selectormax_rows_per_filebasename_templateTaggedRecordBatchRecordBatchReaderParquetFileFormatFileSystemDatasetsegment_encodingpyarrow._datasetinfer_dictionaryHivePartitioningFileWriteOptions__setstate_cython____pyx_PickleErrorphysical_schemadelete_matchingconvert_optionsItem has schema
InMemoryDataset_stringify_pathroot_partition_orc_fileformatmax_partitionsmax_open_filesDictionaryTypeDatasetFactoryArrowTypeErrorwrite_optionsright_dataset__reduce_cython__parse_optionsnull_fallbackmake_fragmentget_fragmentsfrom_fragmentcoalesce_keysc_field_namesStopIterationOrcFileFormatIpcFileFormatFutureWarningCsvFileFormatsubclassesstringsourcestaticmethodscan_batchesright_suffixrecord_batchread_options__pyx_checksumpyarrow.util_perform_joinpartitioning_orc_imported_is_path_likefrom_datasetfrom_batchesfile_visitorfile_optionsdictionariesUnionDatasetPartitioningFileFragmentuse_threadspyarrow.liboutput_typememory_poolleft_suffix_is_iterablefield_namescollectionsWrittenFileRecordBatchPickleErrorImportErrorvisit_argsto_batches__subclasses__right_keys__pyx_vtable____pyx_resultpartitionsnamedtupleleft outerfrom_pathsfilesystem_dataset_pqcreate_dircount_rowsbatch_sizebatch_iterValueErrorFileFormatuse_asynctype_name__reduce_ex____pyx_state partition=[__metaclass__join_typein-memoryfrombytesfragmentsfactories_exec_planenumeratedirectoryc_scannerc_optionsc_batchesTypeErrorwarningsto_table__setstate____qualname____pyx_typenum_rowsmetadata_iterator__getstate__fragmentfilenamediscoverchildrenbase_dirFragmenttobytesscannerref_valpyarrow__prepare__parquetoptionsindicesfeatherdatasetcolumnsbuilderScannerDatasetvaluesupdatetypesourceschema_scalar__reduce__readerpicklename__module____import__formatfilterequalsbufferunionthrowsuper__slots__rangepathsklassitemserrorclose__class__batchas_pyarrayTableArraywarn type=__test__takesizesendreadpathnone__name____main__keyshiveheadfileexpr__dict__dataargsNonevalurioutorc__new__msgipcget__doc__csvcls>]=_pcpaos' on *sfe, ;à;¬èùÿ$ëùÿ àöùÿ P÷ùÿô øùÿ !ùùÿD!ùùÿh!Ôúùÿ!|ûùÿÈ!ýùÿD%dþùÿp%lÿùÿ%úÿÌ%úÿô%Ôúÿ/úÿJúÿ¬JúÿÐJúÿK´Oúÿ¬yÌOúÿԠPúÿø<Púÿ|Púÿ ÌPúÿDÜPúÿìPúÿQúÿ¨Qúÿ¼<QúÿÐ|QúÿäÜQúÿðQúÿ,Rúÿ@0RúÿTLRúÿhpRúÿ|¬Rúÿ\ÐRúÿpüRúÿ¼Súÿ¼üSúÿÜ<Túÿ|Túÿ$¼TúÿHüTúÿl<Uúÿ|Uúÿ´¼UúÿØüUúÿü<Vúÿ |VúÿD¼VúÿhüVúÿPWúÿ°WúÿÔÌWúÿøXúÿLXúÿ@XúÿdÌXúÿYúÿ¬LYúÿÐYúÿôÌYúÿZúÿ<lZúÿ`¬ZúÿìZúÿ¨,[úÿÌÀ]úÿ4Ü]úÿH^úÿT¬^úÿhü^úÿ_úÿÈ¼_úÿøL`úÿ  aúÿPÌaúÿÌbúÿÀ\cúÿôeúÿ0 leúÿP @fúÿ¤ |húÿÐ ¼húÿô!0iúÿ("júÿl"@kúÿ "¼kúÿ´"lúÿè"ìlúÿ#lmúÿ0#ìmúÿT#núÿ|#¼púÿ¸#prúÿ$súÿ $súÿ@$túÿ`$¼túÿ$<uúÿ°$¼uúÿØ$,vúÿ&Ðwúÿ&yúÿØ&Ðyúÿ,'Üzúÿ¸'|úÿô'¬|úÿ((¼|úÿ<(Ð|úÿP(L}úÿx(Ì}úÿ (l~úÿÀ*@úÿÔ*üúÿ+lúÿ8+úÿ+üúÿð+üúÿ,úÿX,úÿ,܎úÿØ,¼úÿ@-̐úÿT-,úÿ-úÿ¬-üúÿØ-lúÿ.ܒúÿ0.@úÿ\.@úÿ¨.¬úÿÔ.úÿ/úÿ,/ìúÿX/lúÿ¬/üúÿä/\úÿ40ܘúÿ\0lúÿ0Lúÿ¬0úÿÔ0üúÿ1úÿ$1,úÿ81lúÿ|1\ úÿÔ1` úÿè1|¡úÿL2,£úÿ2̤úÿÄ2|¦úÿ3̧úÿD3ܨúÿ3̩úÿä3¬úÿ,4úÿX4®úÿ4¯úÿ°4°úÿÜ4±úÿ5²úÿ45³úÿ`5´úÿ5µúÿ¸5¶úÿä5·úÿ6¸úÿ<6¹úÿh6ºúÿ6»úÿÀ6¼úÿì6½úÿ7¾úÿD7¿úÿp7Àúÿ7ÁúÿÈ7Âúÿô7Ãúÿ 8ÄúÿL8Åúÿx8Æúÿ¤8ÇúÿÐ8Èúÿü8Éúÿ(9ÊúÿT9Ëúÿ9Ìúÿ¬9ÍúÿØ9Îúÿ:Ïúÿ0:Ðúÿ\:Ñúÿ:Òúÿ´:Óúÿà:Ôúÿ;Õúÿ8;Öúÿd;l×úÿ¬;¼Øúÿ<|Ùúÿ<<<Úúÿt<üÚúÿ¬<ÐÛúÿÜ< Üúÿ=pÝúÿ<=|àúÿ¸=áúÿ>¼âúÿp>ÜãúÿÌ>üäúÿ(?æúÿ?<çúÿà?\èúÿ<@|éúÿ@êúÿô@ìëúÿ<ALíúÿA\óúÿB<ôúÿXB0õúÿB öúÿÄB÷úÿCü÷úÿLCùúÿCúúÿäCûúÿ(DüûúÿlDìüúÿ°DüýúÿüDìþúÿ@EÜÿúÿEÌûÿÈEÜûÿFìûÿ`Füûÿ¬FûÿøFûÿDG,ûÿG<ûÿÜGL	ûÿ(H\
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¿èÿ	pvxÏø¦	þÿÿoøÎÿÿÿoðÿÿopÊùÿÿoâú	                                                                                                                                                                                                                                                                                      ÿÿÿÿÿÿÿÿ__pyx_unpickle_WrittenFile(__pyx_type, long __pyx_checksum, __pyx_state)_filesystemdataset_write(Scanner data, base_dir, unicode basename_template, FileSystem filesystem, Partitioning partitioning, FileWriteOptions file_options, int max_partitions, file_visitor, unicode existing_data_behavior, int max_open_files, int max_rows_per_file, int min_rows_per_group, int max_rows_per_group, bool create_dir)

    CFileSystemDataset.Write wrapper
    WrittenFile.__setstate_cython__(self, __pyx_state)WrittenFile.__reduce_cython__(self)_get_partition_keys(Expression partition_expression)

    Extract partition keys (equality constraints between a field and a scalar)
    from an expression as a dict mapping the field's name to its value.

    NB: All expressions yielded by a HivePartitioning or DirectoryPartitioning
    will be conjunctions of equality conditions and are accessible through this
    function. Other subexpressions will be ignored.

    For example, an expression of
    <pyarrow.dataset.Expression ((part == A:string) and (year == 2016:int32))>
    is converted to {'part': 'A', 'year': 2016}
    Scanner.__setstate_cython__(self, __pyx_state)Scanner.__reduce_cython__(self)Scanner.to_reader(self)
Consume this scanner as a RecordBatchReader.

        Returns
        -------
        RecordBatchReader
        Scanner.count_rows(self)

        Count rows matching the scanner filter.

        Returns
        -------
        count : int
        Scanner.head(self, int num_rows)

        Load the first N rows of the dataset.

        Parameters
        ----------
        num_rows : int
            The number of rows to load.

        Returns
        -------
        Table
        Scanner.take(self, indices)

        Select rows of data by index.

        Will only consume as many batches of the underlying dataset as
        needed. Otherwise, this is equivalent to
        ``to_table().take(indices)``.

        Parameters
        ----------
        indices : Array or array-like
            indices of rows to select in the dataset.

        Returns
        -------
        Table
        Scanner.to_table(self)

        Convert a Scanner into a Table.

        Use this convenience utility with care. This will serially materialize
        the Scan result in memory before creating the Table.

        Returns
        -------
        Table
        Scanner.scan_batches(self)

        Consume a Scanner in record batches with corresponding fragments.

        Returns
        -------
        record_batches : iterator of TaggedRecordBatch
        Scanner.to_batches(self)

        Consume a Scanner in record batches.

        Returns
        -------
        record_batches : iterator of RecordBatch
        Scanner.from_batches(source, Schema schema=None, bool use_threads=True, use_async=None, MemoryPool memory_pool=None, columns=None, Expression filter=None, int batch_size=_DEFAULT_BATCH_SIZE, FragmentScanOptions fragment_scan_options=None)

        Create a Scanner from an iterator of batches.

        This creates a scanner which can be used only once. It is
        intended to support writing a dataset (which takes a scanner)
        from a source which can be read only once (e.g. a
        RecordBatchReader or generator).

        Parameters
        ----------
        source : Iterator
            The iterator of Batches.
        schema : Schema
            The schema of the batches.
        columns : list of str or dict, default None
                The columns to project.
        filter : Expression, default None
            Scan will return only the rows matching the filter.
        batch_size : int, default 128Ki
            The maximum row count for scanned record batches.
        use_threads : bool, default True
            If enabled, then maximum parallelism will be used determined by
            the number of available CPU cores.
        use_async : bool, default True
            This flag is deprecated and is being kept for this release for
            backwards compatibility.  It will be removed in the next
            release.
        memory_pool : MemoryPool, default None
            For memory allocations, if required. If not specified, uses the
            default pool.
        fragment_scan_options : FragmentScanOptions
            The fragment scan options.
        Scanner.from_fragment(Fragment fragment, Schema schema=None, bool use_threads=True, use_async=None, MemoryPool memory_pool=None, columns=None, Expression filter=None, int batch_size=_DEFAULT_BATCH_SIZE, FragmentScanOptions fragment_scan_options=None)

        Create Scanner from Fragment,

        Parameters
        ----------
        fragment : Fragment
            fragment to scan.
        schema : Schema, optional
            The schema of the fragment.
        columns : list of str, default None
            The columns to project. This can be a list of column names to
            include (order and duplicates will be preserved), or a dictionary
            with {new_column_name: expression} values for more advanced
            projections.

            The list of columns or expressions may use the special fields
            `__batch_index` (the index of the batch within the fragment), 
            `__fragment_index` (the index of the fragment within the dataset), 
            `__last_in_fragment` (whether the batch is last in fragment), and
            `__filename` (the name of the source file or a description of the 
            source fragment).

            The columns will be passed down to Datasets and corresponding data
            fragments to avoid loading, copying, and deserializing columns
            that will not be required further down the compute chain.
            By default all of the available columns are projected. Raises
            an exception if any of the referenced column names does not exist
            in the dataset's Schema.
        filter : Expression, default None
            Scan will return only the rows matching the filter.
            If possible the predicate will be pushed down to exploit the
            partition information or internal metadata found in the data
            source, e.g. Parquet statistics. Otherwise filters the loaded
            RecordBatches before yielding them.
        batch_size : int, default 128Ki
            The maximum row count for scanned record batches. If scanned
            record batches are overflowing memory then this method can be
            called to reduce their size.
        use_threads : bool, default True
            If enabled, then maximum parallelism will be used determined by
            the number of available CPU cores.
        use_async : bool, default True
            This flag is deprecated and is being kept for this release for
            backwards compatibility.  It will be removed in the next
            release.
        memory_pool : MemoryPool, default None
            For memory allocations, if required. If not specified, uses the
            default pool.
        fragment_scan_options : FragmentScanOptions, default None
            Options specific to a particular scan and fragment type, which
            can change between different scans of the same dataset.
        Scanner.from_dataset(Dataset dataset, bool use_threads=True, use_async=None, MemoryPool memory_pool=None, columns=None, Expression filter=None, int batch_size=_DEFAULT_BATCH_SIZE, FragmentScanOptions fragment_scan_options=None)

        Create Scanner from Dataset,

        Parameters
        ----------
        dataset : Dataset
            Dataset to scan.
        columns : list of str, default None
            The columns to project. This can be a list of column names to
            include (order and duplicates will be preserved), or a dictionary
            with {new_column_name: expression} values for more advanced
            projections.

            The list of columns or expressions may use the special fields
            `__batch_index` (the index of the batch within the fragment), 
            `__fragment_index` (the index of the fragment within the dataset), 
            `__last_in_fragment` (whether the batch is last in fragment), and
            `__filename` (the name of the source file or a description of the 
            source fragment).

            The columns will be passed down to Datasets and corresponding data
            fragments to avoid loading, copying, and deserializing columns
            that will not be required further down the compute chain.
            By default all of the available columns are projected. Raises
            an exception if any of the referenced column names does not exist
            in the dataset's Schema.
        filter : Expression, default None
            Scan will return only the rows matching the filter.
            If possible the predicate will be pushed down to exploit the
            partition information or internal metadata found in the data
            source, e.g. Parquet statistics. Otherwise filters the loaded
            RecordBatches before yielding them.
        batch_size : int, default 128Ki
            The maximum row count for scanned record batches. If scanned
            record batches are overflowing memory then this method can be
            called to reduce their size.
        use_threads : bool, default True
            If enabled, then maximum parallelism will be used determined by
            the number of available CPU cores.
        use_async : bool, default True
            This flag is deprecated and is being kept for this release for
            backwards compatibility.  It will be removed in the next
            release.
        memory_pool : MemoryPool, default None
            For memory allocations, if required. If not specified, uses the
            default pool.
        fragment_scan_options : FragmentScanOptions, default None
            Options specific to a particular scan and fragment type, which
            can change between different scans of the same dataset.
        TaggedRecordBatchIterator.__setstate_cython__(self, __pyx_state)TaggedRecordBatchIterator.__reduce_cython__(self)RecordBatchIterator.__setstate_cython__(self, __pyx_state)RecordBatchIterator.__reduce_cython__(self)UnionDatasetFactory.__setstate_cython__(self, __pyx_state)UnionDatasetFactory.__reduce_cython__(self)FileSystemDatasetFactory.__setstate_cython__(self, __pyx_state)FileSystemDatasetFactory.__reduce_cython__(self)FileSystemFactoryOptions.__setstate_cython__(self, __pyx_state)FileSystemFactoryOptions.__reduce_cython__(self)DatasetFactory.__setstate_cython__(self, __pyx_state)DatasetFactory.__reduce_cython__(self)DatasetFactory.finish(self, Schema schema=None)

        Create a Dataset using the inspected schema or an explicit schema
        (if given).

        Parameters
        ----------
        schema : Schema, default None
            The schema to conform the source to.  If None, the inspected
            schema is used.

        Returns
        -------
        Dataset
        DatasetFactory.inspect(self)

        Inspect all data fragments and return a common Schema.

        Returns
        -------
        Schema
        DatasetFactory.inspect_schemas(self)FilenamePartitioning.__setstate_cython__(self, __pyx_state)FilenamePartitioning.__reduce_cython__(self)FilenamePartitioning.discover(field_names=None, infer_dictionary=False, schema=None, segment_encoding=u'uri')

        Discover a FilenamePartitioning.

        Parameters
        ----------
        field_names : list of str
            The names to associate with the values from the subdirectory names.
            If schema is given, will be populated from the schema.
        infer_dictionary : bool, default False
            When inferring a schema for partition fields, yield dictionary
            encoded types instead of plain types. This can be more efficient
            when materializing virtual columns, and Expressions parsed by the
            finished Partitioning will include dictionaries of all unique
            inspected values for each field.
        schema : Schema, default None
            Use this schema instead of inferring a schema from partition
            values. Partition values will be validated against this schema
            before accumulation into the Partitioning's dictionary.
        segment_encoding : str, default "uri"
            After splitting paths into segments, decode the segments. Valid
            values are "uri" (URI-decode segments) and "none" (leave as-is).

        Returns
        -------
        PartitioningFactory
            To be used in the FileSystemFactoryOptions.
        HivePartitioning.__setstate_cython__(self, __pyx_state)HivePartitioning.__reduce_cython__(self)HivePartitioning.discover(infer_dictionary=False, max_partition_dictionary_size=0, null_fallback=u'__HIVE_DEFAULT_PARTITION__', schema=None, segment_encoding=u'uri')

        Discover a HivePartitioning.

        Parameters
        ----------
        infer_dictionary : bool, default False
            When inferring a schema for partition fields, yield dictionary
            encoded types instead of plain. This can be more efficient when
            materializing virtual columns, and Expressions parsed by the
            finished Partitioning will include dictionaries of all unique
            inspected values for each field.
        max_partition_dictionary_size : int, default 0
            Synonymous with infer_dictionary for backwards compatibility with
            1.0: setting this to -1 or None is equivalent to passing
            infer_dictionary=True.
        null_fallback : str, default "__HIVE_DEFAULT_PARTITION__"
            When inferring a schema for partition fields this value will be
            replaced by null.  The default is set to __HIVE_DEFAULT_PARTITION__
            for compatibility with Spark
        schema : Schema, default None
            Use this schema instead of inferring a schema from partition
            values. Partition values will be validated against this schema
            before accumulation into the Partitioning's dictionary.
        segment_encoding : str, default "uri"
            After splitting paths into segments, decode the segments. Valid
            values are "uri" (URI-decode segments) and "none" (leave as-is).

        Returns
        -------
        PartitioningFactory
            To be used in the FileSystemFactoryOptions.
        DirectoryPartitioning.__setstate_cython__(self, __pyx_state)DirectoryPartitioning.__reduce_cython__(self)DirectoryPartitioning.discover(field_names=None, infer_dictionary=False, max_partition_dictionary_size=0, schema=None, segment_encoding=u'uri')

        Discover a DirectoryPartitioning.

        Parameters
        ----------
        field_names : list of str
            The names to associate with the values from the subdirectory names.
            If schema is given, will be populated from the schema.
        infer_dictionary : bool, default False
            When inferring a schema for partition fields, yield dictionary
            encoded types instead of plain types. This can be more efficient
            when materializing virtual columns, and Expressions parsed by the
            finished Partitioning will include dictionaries of all unique
            inspected values for each field.
        max_partition_dictionary_size : int, default 0
            Synonymous with infer_dictionary for backwards compatibility with
            1.0: setting this to -1 or None is equivalent to passing
            infer_dictionary=True.
        schema : Schema, default None
            Use this schema instead of inferring a schema from partition
            values. Partition values will be validated against this schema
            before accumulation into the Partitioning's dictionary.
        segment_encoding : str, default "uri"
            After splitting paths into segments, decode the segments. Valid
            values are "uri" (URI-decode segments) and "none" (leave as-is).

        Returns
        -------
        PartitioningFactory
            To be used in the FileSystemFactoryOptions.
        KeyValuePartitioning.__setstate_cython__(self, __pyx_state)KeyValuePartitioning.__reduce_cython__(self)PartitioningFactory.__setstate_cython__(self, __pyx_state)PartitioningFactory.__reduce_cython__(self)Partitioning.__setstate_cython__(self, __pyx_state)Partitioning.__reduce_cython__(self)Partitioning.parse(self, path)CsvFileWriteOptions.__setstate_cython__(self, __pyx_state)CsvFileWriteOptions.__reduce_cython__(self)CsvFragmentScanOptions.__reduce__(self)CsvFragmentScanOptions.equals(self, CsvFragmentScanOptions other)CsvFileFormat.__reduce__(self)CsvFileFormat.equals(self, CsvFileFormat other)CsvFileFormat.make_write_options(self, **kwargs)IpcFileFormat.__reduce__(self)IpcFileFormat.equals(self, IpcFileFormat other)IpcFileWriteOptions.__setstate_cython__(self, __pyx_state)IpcFileWriteOptions.__reduce_cython__(self)FragmentScanOptions.__setstate_cython__(self, __pyx_state)FragmentScanOptions.__reduce_cython__(self)FileFragment.__reduce__(self)Fragment.__setstate_cython__(self, __pyx_state)Fragment.__reduce_cython__(self)Fragment.count_rows(self, **kwargs)

        Count rows matching the scanner filter.

        Parameters
        ----------
        **kwargs : dict, optional
            Arguments for `Scanner.from_fragment`.

        Returns
        -------
        count : int
        Fragment.head(self, int num_rows, **kwargs)

        Load the first N rows of the fragment.

        Parameters
        ----------
        num_rows : int
            The number of rows to load.
        **kwargs : dict, optional
            Arguments for `Scanner.from_fragment`.

        Returns
        -------
        Table
        Fragment.take(self, indices, **kwargs)

        Select rows of data by index.

        Parameters
        ----------
        indices : Array or array-like
            The indices of row to select in the dataset.
        **kwargs : dict, optional
            Arguments for `Scanner.from_fragment`.

        Returns
        -------
        Table
        Fragment.to_table(self, Schema schema=None, **kwargs)

        Convert this Fragment into a Table.

        Use this convenience utility with care. This will serially materialize
        the Scan result in memory before creating the Table.

        Parameters
        ----------
        schema : Schema, optional
            Concrete schema to use for scanning.
        **kwargs : dict, optional
            Arguments for `Scanner.from_fragment`.

        Returns
        -------
        table : Table
        Fragment.to_batches(self, Schema schema=None, **kwargs)

        Read the fragment as materialized record batches.

        Parameters
        ----------
        schema : Schema, optional
            Concrete schema to use for scanning.
        **kwargs : dict, optional
            Arguments for `Scanner.from_fragment`.

        Returns
        -------
        record_batches : iterator of RecordBatch
        Fragment.scanner(self, Schema schema=None, **kwargs)

        Build a scan operation against the fragment.

        Data is not loaded immediately. Instead, this produces a Scanner,
        which exposes further operations (e.g. loading all data as a
        table, counting rows).

        Parameters
        ----------
        schema : Schema
            Schema to use for scanning. This is used to unify a Fragment to
            it's Dataset's schema. If not specified this will use the
            Fragment's physical schema which might differ for each Fragment.
        **kwargs : dict, optional
            Arguments for `Scanner.from_fragment`.

        Returns
        -------
        scanner : Scanner
        FileFormat.__setstate_cython__(self, __pyx_state)FileFormat.__reduce_cython__(self)FileFormat.make_write_options(self)FileFormat.make_fragment(self, file, filesystem=None, Expression partition_expression=None)

        Make a FileFragment from a given file.

        Parameters
        ----------
        file : file-like object, path-like or str
            The file or file path to make a fragment from.
        filesystem : Filesystem, optional
            If `filesystem` is given, `file` must be a string and specifies
            the path of the file to read from the filesystem.
        partition_expression : Expression
            The filter expression.
        FileFormat.inspect(self, file, filesystem=None)

        Infer the schema of a file.

        Parameters
        ----------
        file : file-like object, path-like or str
            The file or file path to infer a schema from.
        filesystem : Filesystem, optional
            If `filesystem` is given, `file` must be a string and specifies
            the path of the file to read from the filesystem.

        Returns
        -------
        schema : Schema
            The schema inferred from the file
        FileWriteOptions.__setstate_cython__(self, __pyx_state)FileWriteOptions.__reduce_cython__(self)FileSystemDataset.from_paths(type cls, paths, schema=None, format=None, filesystem=None, partitions=None, root_partition=None)
A Dataset created from a list of paths on a particular filesystem.

        Parameters
        ----------
        paths : list of str
            List of file paths to create the fragments from.
        schema : Schema
            The top-level schema of the DataDataset.
        format : FileFormat
            File format to create fragments from, currently only
            ParquetFileFormat, IpcFileFormat, and CsvFileFormat are supported.
        filesystem : FileSystem
            The filesystem which files are from.
        partitions : list[Expression], optional
            Attach additional partition information for the file paths.
        root_partition : Expression, optional
            The top-level partition of the DataDataset.
        FileSystemDataset.__reduce__(self)UnionDataset.__reduce__(self)InMemoryDataset.__setstate_cython__(self, __pyx_state)InMemoryDataset.__reduce_cython__(self)Dataset.__setstate_cython__(self, __pyx_state)Dataset.__reduce_cython__(self)Dataset.join(self, right_dataset, keys, right_keys=None, join_type=u'left outer', left_suffix=None, right_suffix=None, coalesce_keys=True, use_threads=True)

        Perform a join between this dataset and another one.

        Result of the join will be a new dataset, where further
        operations can be applied.

        Parameters
        ----------
        right_dataset : dataset
            The dataset to join to the current one, acting as the right dataset
            in the join operation.
        keys : str or list[str]
            The columns from current dataset that should be used as keys
            of the join operation left side.
        right_keys : str or list[str], default None
            The columns from the right_dataset that should be used as keys
            on the join operation right side.
            When ``None`` use the same key names as the left dataset.
        join_type : str, default "left outer"
            The kind of join that should be performed, one of
            ("left semi", "right semi", "left anti", "right anti",
            "inner", "left outer", "right outer", "full outer")
        left_suffix : str, default None
            Which suffix to add to right column names. This prevents confusion
            when the columns in left and right datasets have colliding names.
        right_suffix : str, default None
            Which suffic to add to the left column names. This prevents confusion
            when the columns in left and right datasets have colliding names.
        coalesce_keys : bool, default True
            If the duplicated keys should be omitted from one of the sides
            in the join result.
        use_threads : bool, default True
            Whenever to use multithreading or not.

        Returns
        -------
        InMemoryDataset
        Dataset.count_rows(self, **kwargs)

        Count rows matching the scanner filter.

        Parameters
        ----------
        **kwargs : dict, optional
            See scanner() method for full parameter description.

        Returns
        -------
        count : int
        Dataset.head(self, int num_rows, **kwargs)

        Load the first N rows of the dataset.

        Parameters
        ----------
        num_rows : int
            The number of rows to load.
        **kwargs : dict, optional
            See scanner() method for full parameter description.

        Returns
        -------
        table : Table
        Dataset.take(self, indices, **kwargs)

        Select rows of data by index.

        Parameters
        ----------
        indices : Array or array-like
            indices of rows to select in the dataset.
        **kwargs : dict, optional
            See scanner() method for full parameter description.

        Returns
        -------
        table : Table
        Dataset.to_table(self, **kwargs)

        Read the dataset to an Arrow table.

        Note that this method reads all the selected data from the dataset
        into memory.

        Parameters
        ----------
        **kwargs : dict, optional
            Arguments for `Scanner.from_dataset`.

        Returns
        -------
        table : Table
        Dataset.to_batches(self, **kwargs)

        Read the dataset as materialized record batches.

        Parameters
        ----------
        **kwargs : dict, optional
            Arguments for `Scanner.from_dataset`.

        Returns
        -------
        record_batches : iterator of RecordBatch
        Dataset.scanner(self, **kwargs)

        Build a scan operation against the dataset.

        Data is not loaded immediately. Instead, this produces a Scanner,
        which exposes further operations (e.g. loading all data as a
        table, counting rows).

        See the `Scanner.from_dataset` method for further information.

        Parameters
        ----------
        **kwargs : dict, optional
            Arguments for `Scanner.from_dataset`.

        Returns
        -------
        scanner : Scanner

        Examples
        --------
        >>> import pyarrow as pa
        >>> table = pa.table({'year': [2020, 2022, 2021, 2022, 2019, 2021],
        ...                   'n_legs': [2, 2, 4, 4, 5, 100],
        ...                   'animal': ["Flamingo", "Parrot", "Dog", "Horse",
        ...                              "Brittle stars", "Centipede"]})
        >>> 
        >>> import pyarrow.parquet as pq
        >>> pq.write_table(table, "dataset_scanner.parquet")

        >>> import pyarrow.dataset as ds
        >>> dataset = ds.dataset("dataset_scanner.parquet")

        Selecting a subset of the columns:

        >>> dataset.scanner(columns=["year", "n_legs"]).to_table()
        pyarrow.Table
        year: int64
        n_legs: int64
        ----
        year: [[2020,2022,2021,2022,2019,2021]]
        n_legs: [[2,2,4,4,5,100]]

        Projecting selected columns using an expression:

        >>> dataset.scanner(columns={
        ...     "n_legs_uint": ds.field("n_legs").cast("uint8"),
        ... }).to_table()
        pyarrow.Table
        n_legs_uint: uint8
        ----
        n_legs_uint: [[2,2,4,4,5,100]]

        Filtering rows while scanning:

        >>> dataset.scanner(filter=ds.field("year") > 2020).to_table()
        pyarrow.Table
        year: int64
        n_legs: int64
        animal: string
        ----
        year: [[2022,2021,2022,2021]]
        n_legs: [[2,4,4,100]]
        animal: [["Parrot","Dog","Horse","Centipede"]]
        Dataset.get_fragments(self, Expression filter=None)
Returns an iterator over the fragments in this dataset.

        Parameters
        ----------
        filter : Expression, default None
            Return fragments matching the optional filter, either using the
            partition_expression or internal information like Parquet's
            statistics.

        Returns
        -------
        fragments : iterator of Fragment
        Dataset.replace_schema(self, Schema schema)

        Return a copy of this Dataset with a different schema.

        The copy will view the same Fragments. If the new schema is not
        compatible with the original dataset's schema then an error will
        be raised.

        Parameters
        ----------
        schema : Schema
            The new dataset schema.
        _get_parquet_symbol(name)

    Get a symbol from pyarrow.parquet if the latter is importable, otherwise
    return None.
    _get_parquet_classes()

    Import Parquet class files on first usage (to avoid circular import issue
    when `pyarrow._dataset_parquet` would be imported first)
    _get_orc_fileformat()

    Import OrcFileFormat on first usage (to avoid circular import issue
    when `pyarrow._dataset_orc` would be imported first)
    _forbid_instantiation(klass, subclasses_instead=True)¨°¸pB¸ð!pä[@¤"´(й
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