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arrow-nightlies / pyarrow python
Repository URL to install this package:
|
Version:
22.0.0.dev96 ▾
|
pyarrow
/
_dataset.cpython-39-darwin.so
|
|---|
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ÿ% , , 3 Kc q, ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ ð? ð¿ _ Dataset is currently unstable. APIs subject to change without notice. name pyarrow._dataset pyarrow/_dataset.pyx <stringsource>
A Source node which yields batches from a Dataset scan.
This is the option class for the "scan" node factory.
This node is capable of applying pushdown projections or filters
to the file readers which reduce the amount of data that needs to
be read (if supported by the file format). But note that this does not
construct associated filter or project nodes to perform the final
filtering or projection. Rather, you may supply the same filter
expression or projection to the scan node that you also supply
to the filter or project node.
Yielded batches will be augmented with fragment/batch indices when
implicit_ordering=True to enable stable ordering for simple ExecPlans.
Parameters
----------
dataset : pyarrow.dataset.Dataset
The table which acts as the data source.
**kwargs : dict, optional
Scan options. See `Scanner.from_dataset` for possible arguments.
require_sequenced_output : bool, default False
Batches are yielded sequentially, like single-threaded
implicit_ordering : bool, default False
Preserve implicit ordering of data.
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.
Argument ' Argument 'root_partition' has incorrect type (expected Expression, got ArrowTypeError AssertionError BoundExpressions BufferReader
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 :meth:`Scanner.from_dataset` method for further information.
Parameters
----------
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 131_072
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.
batch_readahead : int, default 16
The number of batches to read ahead in a file. This might not work
for all file formats. Increasing this number will increase
RAM usage but could also improve IO utilization.
fragment_readahead : int, default 4
The number of files to read ahead. Increasing this number will increase
RAM usage but could also improve IO utilization.
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.
use_threads : bool, default True
If enabled, then maximum parallelism will be used determined by
the number of available CPU cores.
cache_metadata : bool, default True
If enabled, metadata may be cached when scanning to speed up
repeated scans.
memory_pool : MemoryPool, default None
For memory allocations, if required. If not specified, uses the
default pool.
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"]]
Cannot set fragment scan options for ' Cannot specify a schema when providing a RecordBatchReader Cannot specify a schema when providing an object implementing the Arrow PyCapsule Protocol
Convert a filter expression into a tuple of (directory, filename) using
the current partitioning scheme
Parameters
----------
expr : pyarrow.dataset.Expression
Returns
-------
tuple[str, str]
Examples
--------
Specify the Schema for paths like "/2009/June":
>>> import pyarrow as pa
>>> import pyarrow.dataset as ds
>>> import pyarrow.compute as pc
>>> part = ds.partitioning(pa.schema([("year", pa.int16()),
... ("month", pa.string())]))
>>> part.format(
... (pc.field("year") == 1862) & (pc.field("month") == "Jan")
... )
('1862/Jan', '')
CsvFileFormat CsvFileFormat.__reduce__ CsvFileFormat.equals CsvFileFormat.make_write_options <CsvFileFormat parse_options= CsvFileWriteOptions CsvFileWriteOptions.__reduce_cython__ CsvFileWriteOptions.__setstate_cython__ CsvFragmentScanOptions CsvFragmentScanOptions.__reduce__ CsvFragmentScanOptions.equals _DEFAULT_BATCH_READAHEAD _DEFAULT_BATCH_SIZE _DEFAULT_FRAGMENT_READAHEAD Dataset DatasetFactory DatasetFactory.__reduce_cython__ DatasetFactory.__setstate_cython__ DatasetFactory.finish DatasetFactory.inspect DatasetFactory.inspect_schemas Dataset.__reduce_cython__ Dataset.__setstate_cython__ Dataset._get_fragments Dataset._scanner_options Dataset.count_rows Dataset.filter Dataset.get_fragments Dataset.head Dataset.join Dataset.join_asof Dataset.replace_schema Dataset.scanner Dataset.scanner (line 295) Dataset.sort_by Dataset.take Dataset.to_batches Dataset.to_table DirectoryPartitioning DirectoryPartitioning.discover Expected a RecordBatchReader or an iterable of batches instead of the given type: Expected a Table, RecordBatch, list of Table/RecordBatch, or RecordBatchReader instead of the given type: Expected a list of tables or batches. The given list contains a Expected a list or a dict for 'columns', got Expected an Expression for a 'column' dictionary value, got FeatherFileFormat FileFormat FileFormat.__reduce_cython__ FileFormat.__setstate_cython__ FileFormat.inspect FileFormat.make_fragment FileFormat.make_write_options FileFragment FileFragment.__reduce__ FileFragment.open FileSystemDataset FileSystemDatasetFactory FileSystemDatasetFactory.__reduce_cython__ FileSystemDatasetFactory.__setstate_cython__ FileSystemDataset.__reduce__ FileSystemDataset.from_paths FileSystemFactoryOptions FileSystemFactoryOptions.__reduce_cython__ FileSystemFactoryOptions.__setstate_cython__ FileWriteOptions FileWriteOptions.__reduce_cython__ FileWriteOptions.__setstate_cython__ FilenamePartitioning FilenamePartitioning.discover Fragment FragmentScanOptions FragmentScanOptions.__reduce_cython__ FragmentScanOptions.__setstate_cython__ Fragment.__reduce_cython__ Fragment.__setstate_cython__ Fragment count must be a non-negative int or None; got Fragment.count_rows Fragment.head Fragment.scanner Fragment.take Fragment.to_batches Fragment.to_table __HIVE_DEFAULT_PARTITION__ HivePartitioning HivePartitioning.__reduce__ HivePartitioning.discover If `default_fragment_scan_options` is given, cannot specify convert_options or read_options If `default_fragment_scan_options` is given, cannot specify read_options ImportError InMemoryDataset InMemoryDataset.__reduce_cython__ InMemoryDataset.__setstate_cython__ Incompatible checksums (0x%x vs (0x2aeeab1, 0xb9a0db3, 0x14f8208) = (metadata, path, size)) IpcFileFormat IpcFileFormat.__reduce__ IpcFileFormat.equals IpcFileFormat.make_write_options IpcFileWriteOptions IpcFileWriteOptions.__reduce_cython__ IpcFileWriteOptions.__setstate_cython__ Item has schema
<JsonFileFormat> JsonFileFormat JsonFileFormat.__reduce__ JsonFileFormat.equals JsonFragmentScanOptions JsonFragmentScanOptions.__reduce__ JsonFragmentScanOptions.equals KeyValuePartitioning KeyValuePartitioning.__reduce__ Must pass either paths or a FileSelector, but passed Must provide schema to construct in-memory dataset from an empty list Must provide schema to construct scanner from an iterable Neither field_names nor schema was passed; cannot infer field_names None NotImplementedError Note that Cython is deliberately stricter than PEP-484 and rejects subclasses of builtin types. If you need to pass subclasses then set the 'annotation_typing' directive to False. Only one BoundExpressions with a single expression are supported OrcFileFormat ParquetFileFormat ParquetFileFragment ParquetFileWriteOptions ParquetFragmentScanOptions Partitioning PartitioningFactory PartitioningFactory.__reduce__ Partitioning.__reduce_cython__ Partitioning.__setstate_cython__ Partitioning.format Partitioning.format (line 2587) Partitioning.parse PickleError RecordBatchIterator RecordBatchIterator.__reduce_cython__ RecordBatchIterator.__setstate_cython__ Retrieving fragments of a filtered or projected dataset is not allowed. Remove the filtering. _ScanNodeOptions ScanNodeOptions ScanNodeOptions.__init__ _ScanNodeOptions.__reduce_cython__ _ScanNodeOptions.__setstate_cython__ _ScanNodeOptions._set_options Scanner Scanner.__reduce_cython__ Scanner.__setstate_cython__ Scanner.count_rows Scanner.from_batches Scanner.from_dataset Scanner.from_fragment Scanner.head Scanner.scan_batches Scanner.take Scanner.to_batches Scanner.to_reader Scanner.to_table StopIteration TaggedRecordBatch TaggedRecordBatchIterator TaggedRecordBatchIterator.__reduce_cython__ TaggedRecordBatchIterator.__setstate_cython__ The number of files resulting from paths_or_selector must be equal to the number of partitions. TypeError UnionDataset UnionDatasetFactory UnionDatasetFactory.__reduce_cython__ UnionDatasetFactory.__setstate_cython__ UnionDataset.__reduce__ Unknown segment encoding: Unknown segment encoding ValueError Writing datasets not yet implemented for this file format. WrittenFile WrittenFile.__reduce_cython__ WrittenFile.__setstate_cython__ ) , = ] > ? add_note arg args array arrow __arrow_c_stream__ as_py ascending asyncio.coroutines batch batch_iter batch_readahead batch_size builder by c_field_names c_filesystem c_filter c_fragment c_fragments c_indices c_options c_path c_result c_scan_options c_scanner c_schema c_source cache_metadata cannot construct a FileSource from a path without a FileSystem cannot construct a FileSource from __class__ class_ __class_getitem__ cline_in_traceback cls coalesce_keys codecs collections columns create_dir csv current_filter d _dataset_parquet _dataset_pq default `default_fragment_scan_options` must be either a dictionary or an instance of CsvFragmentScanOptions `default_fragment_scan_options` must be either a dictionary or an instance of JsonFragmentScanOptions delete_matching _dict directory disable e enable enumerate error existing_data_behavior must be one of 'error', expressions f feather field_names file file_size file_visitor filename filtered_dataset _forbid_instantiation fragment_readahead fragments from_stream frombytes __func__ gc __getstate__ , got ' has incorrect type (expected hive i implicit_ordering in-memory indices infer_dictionary _initializing instead instead. ipc _is_iterable _is_path_like isenabled iterator join_type json keys kwargs left outer left_suffix lookup __main__ max_open_files max_partition_dictionary_size max_partition_dictionary_size must be 0, -1, or None max_partitions max_rows_per_file max_rows_per_group maybe_fragment __metaclass__ min_rows_per_group __mro_entries__ namedtuple names __new__ new_filter new_options next none null_fallback num_rows on ' on opened opts orc _orc_fileformat _orc_imported out output_type overwrite_or_ignore 'overwrite_or_ignore' or 'delete_matching' pa pa_substrait _pac parquet partition=[ partitions path= paths paths_or_selector _perform_join _perform_join_asof pickle pop __prepare__ preserve_order promote_options pyarrow pyarrow._dataset_orc pyarrow._dataset_parquet <pyarrow.dataset. pyarrow.substrait pyarrow.util __pyx_PickleError __pyx_checksum __pyx_result __pyx_state __pyx_type __pyx_vtable__ range read reader record_batch ref_val requested_filter require_sequenced_output res result right_by right_dataset right_keys right_on right_suffix _scalar schemas segment_encoding self self.c_options,self.csv_options,self.wrapped cannot be converted to a Python object for pickling self.c_options,self.ipc_options,self.wrapped cannot be converted to a Python object for pickling self.c_options,self.wrapped cannot be converted to a Python object for pickling self.dataset,self.in_memory_dataset,self.wrapped cannot be converted to a Python object for pickling self.dataset,self.wrapped cannot be converted to a Python object for pickling self.factory,self.filesystem_factory,self.wrapped cannot be converted to a Python object for pickling self.factory,self.union_factory,self.wrapped cannot be converted to a Python object for pickling self.factory,self.wrapped cannot be converted to a Python object for pickling self.format,self.wrapped cannot be converted to a Python object for pickling self.fragment,self.wrapped cannot be converted to a Python object for pickling self.iterator cannot be converted to a Python object for pickling self.options cannot be converted to a Python object for pickling self.partitioning,self.wrapped cannot be converted to a Python object for pickling self.scanner,self.wrapped cannot be converted to a Python object for pickling self.wrapped cannot be converted to a Python object for pickling _set_default_fragment_scan_options __set_name__ __setstate__ __slots__ sort_keys _sort_source sorting source sp_schema sp_write_options __spec__ state staticmethod _stringify_path subclasses_instead substrait super __test__ to_batches.<locals>._iterator tobytes tolerance type= union update uri use_setstate use_threads utf-8 val visit_args
which does not match expected schema
zip __module__ __reduce__ __doc__ __name__ __qualname__ __dict__ _is_coroutine send throw close __reduce_ex__ NSt3__110__function6__funcIPFN5arrow6ResultINS_10shared_ptrINS2_2io16RandomAccessFileEEEEEvENS_9allocatorISA_EES9_EE NSt3__110__function6__baseIFN5arrow6ResultINS_10shared_ptrINS2_2io16RandomAccessFileEEEEEvEEE PFN5arrow6ResultINSt3__110shared_ptrINS_2io16RandomAccessFileEEEEEvE FN5arrow6ResultINSt3__110shared_ptrINS_2io16RandomAccessFileEEEEEvE pyarrow.lib inspect_schemas DatasetFactory.inspect_schemas(self) inspect DatasetFactory.inspect(self, *, promote_options='default', fragments=None)
Inspect data fragments and return a common Schema.
Parameters
----------
promote_options : str, default "default"
Control how to unify types. Accepts strings "default" and "permissive".
Default: types must match exactly, except nulls can be merged with other types.
Permissive: types are promoted when possible.
fragments : int, default None
How many fragments should be inspected to infer the unified schema.
Use ``None`` to inspect all fragments.
Returns
-------
Schema finish 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 __reduce_cython__ DatasetFactory.__reduce_cython__(self) __setstate_cython__ DatasetFactory.__setstate_cython__(self, __pyx_state) s schema root_partition expr __init__ replace_schema 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_fragments 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 _get_fragments Dataset._get_fragments(self, Expression filter) _scanner_options Dataset._scanner_options(self, options)
Returns the default options to create a new Scanner.
This is automatically invoked by :meth:`Dataset.scanner`
and there is no need to use it. scanner Dataset.scanner(self, columns=None, filter=None, int batch_size=_DEFAULT_BATCH_SIZE, int batch_readahead=_DEFAULT_BATCH_READAHEAD, int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, FragmentScanOptions fragment_scan_options=None, bool use_threads=True, bool cache_metadata=True, MemoryPool memory_pool=None)
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 :meth:`Scanner.from_dataset` method for further information.
Parameters
----------
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 131_072
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.
batch_readahead : int, default 16
The number of batches to read ahead in a file. This might not work
for all file formats. Increasing this number will increase
RAM usage but could also improve IO utilization.
fragment_readahead : int, default 4
The number of files to read ahead. Increasing this number will increase
RAM usage but could also improve IO utilization.
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.
use_threads : bool, default True
If enabled, then maximum parallelism will be used determined by
the number of available CPU cores.
cache_metadata : bool, default True
If enabled, metadata may be cached when scanning to speed up
repeated scans.
memory_pool : MemoryPool, default None
For memory allocations, if required. If not specified, uses the
default pool.
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"]] to_batches Dataset.to_batches(self, columns=None, Expression filter=None, int batch_size=_DEFAULT_BATCH_SIZE, int batch_readahead=_DEFAULT_BATCH_READAHEAD, int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, FragmentScanOptions fragment_scan_options=None, bool use_threads=True, bool cache_metadata=True, MemoryPool memory_pool=None)
Read the dataset as materialized record batches.
Parameters
----------
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 131_072
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.
batch_readahead : int, default 16
The number of batches to read ahead in a file. This might not work
for all file formats. Increasing this number will increase
RAM usage but could also improve IO utilization.
fragment_readahead : int, default 4
The number of files to read ahead. Increasing this number will increase
RAM usage but could also improve IO utilization.
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.
use_threads : bool, default True
If enabled, then maximum parallelism will be used determined by
the number of available CPU cores.
cache_metadata : bool, default True
If enabled, metadata may be cached when scanning to speed up
repeated scans.
memory_pool : MemoryPool, default None
For memory allocations, if required. If not specified, uses the
default pool.
Returns
-------
record_batches : iterator of RecordBatch to_table Dataset.to_table(self, columns=None, Expression filter=None, int batch_size=_DEFAULT_BATCH_SIZE, int batch_readahead=_DEFAULT_BATCH_READAHEAD, int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, FragmentScanOptions fragment_scan_options=None, bool use_threads=True, bool cache_metadata=True, MemoryPool memory_pool=None)
Read the dataset to an Arrow table.
Note that this method reads all the selected data from the dataset
into memory.
Parameters
----------
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 131_072
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.
batch_readahead : int, default 16
The number of batches to read ahead in a file. This might not work
for all file formats. Increasing this number will increase
RAM usage but could also improve IO utilization.
fragment_readahead : int, default 4
The number of files to read ahead. Increasing this number will increase
RAM usage but could also improve IO utilization.
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.
use_threads : bool, default True
If enabled, then maximum parallelism will be used determined by
the number of available CPU cores.
cache_metadata : bool, default True
If enabled, metadata may be cached when scanning to speed up
repeated scans.
memory_pool : MemoryPool, default None
For memory allocations, if required. If not specified, uses the
default pool.
Returns
-------
table : Table take Dataset.take(self, indices, columns=None, Expression filter=None, int batch_size=_DEFAULT_BATCH_SIZE, int batch_readahead=_DEFAULT_BATCH_READAHEAD, int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, FragmentScanOptions fragment_scan_options=None, bool use_threads=True, bool cache_metadata=True, MemoryPool memory_pool=None)
Select rows of data by index.
Parameters
----------
indices : Array or array-like
indices of rows to select in the dataset.
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 131_072
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.
batch_readahead : int, default 16
The number of batches to read ahead in a file. This might not work
for all file formats. Increasing this number will increase
RAM usage but could also improve IO utilization.
fragment_readahead : int, default 4
The number of files to read ahead. Increasing this number will increase
RAM usage but could also improve IO utilization.
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.
use_threads : bool, default True
If enabled, then maximum parallelism will be used determined by
the number of available CPU cores.
cache_metadata : bool, default True
If enabled, metadata may be cached when scanning to speed up
repeated scans.
memory_pool : MemoryPool, default None
For memory allocations, if required. If not specified, uses the
default pool.
Returns
-------
table : Table head Dataset.head(self, int num_rows, columns=None, Expression filter=None, int batch_size=_DEFAULT_BATCH_SIZE, int batch_readahead=_DEFAULT_BATCH_READAHEAD, int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, FragmentScanOptions fragment_scan_options=None, bool use_threads=True, bool cache_metadata=True, MemoryPool memory_pool=None)
Load the first N rows of the dataset.
Parameters
----------
num_rows : int
The number of rows to load.
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 131_072
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.
batch_readahead : int, default 16
The number of batches to read ahead in a file. This might not work
for all file formats. Increasing this number will increase
RAM usage but could also improve IO utilization.
fragment_readahead : int, default 4
The number of files to read ahead. Increasing this number will increase
RAM usage but could also improve IO utilization.
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.
use_threads : bool, default True
If enabled, then maximum parallelism will be used determined by
the number of available CPU cores.
cache_metadata : bool, default True
If enabled, metadata may be cached when scanning to speed up
repeated scans.
memory_pool : MemoryPool, default None
For memory allocations, if required. If not specified, uses the
default pool.
Returns
-------
table : Table count_rows Dataset.count_rows(self, Expression filter=None, int batch_size=_DEFAULT_BATCH_SIZE, int batch_readahead=_DEFAULT_BATCH_READAHEAD, int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, FragmentScanOptions fragment_scan_options=None, bool use_threads=True, bool cache_metadata=True, MemoryPool memory_pool=None)
Count rows matching the scanner filter.
Parameters
----------
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 131_072
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.
batch_readahead : int, default 16
The number of batches to read ahead in a file. This might not work
for all file formats. Increasing this number will increase
RAM usage but could also improve IO utilization.
fragment_readahead : int, default 4
The number of files to read ahead. Increasing this number will increase
RAM usage but could also improve IO utilization.
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.
use_threads : bool, default True
If enabled, then maximum parallelism will be used determined by
the number of available CPU cores.
cache_metadata : bool, default True
If enabled, metadata may be cached when scanning to speed up
repeated scans.
memory_pool : MemoryPool, default None
For memory allocations, if required. If not specified, uses the
default pool.
Returns
-------
count : int filter Dataset.filter(self, expression)
Apply a row filter to the dataset.
Parameters
----------
expression : Expression
The filter that should be applied to the dataset.
Returns
-------
Dataset sort_by Dataset.sort_by(self, sorting, **kwargs)
Sort the Dataset by one or multiple columns.
Parameters
----------
sorting : str or list[tuple(name, order)]
Name of the column to use to sort (ascending), or
a list of multiple sorting conditions where
each entry is a tuple with column name
and sorting order ("ascending" or "descending")
**kwargs : dict, optional
Additional sorting options.
As allowed by :class:`SortOptions`
Returns
-------
InMemoryDataset
A new dataset sorted according to the sort keys. join Dataset.join(self, right_dataset, keys, right_keys=None, join_type='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 suffix 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 join_asof Dataset.join_asof(self, right_dataset, on, by, tolerance, right_on=None, right_by=None)
Perform an asof join between this dataset and another one.
This is similar to a left-join except that we match on nearest key rather
than equal keys. Both datasets must be sorted by the key. This type of join
is most useful for time series data that are not perfectly aligned.
Optionally match on equivalent keys with "by" before searching with "on".
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.
on : str
The column from current dataset that should be used as the "on" key
of the join operation left side.
An inexact match is used on the "on" key, i.e. a row is considered a
match if and only if ``right.on - left.on`` is in the range
``[min(0, tolerance), max(0, tolerance)]``.
The input table must be sorted by the "on" key. Must be a single
field of a common type.
Currently, the "on" key must be an integer, date, or timestamp type.
by : str or list[str]
The columns from current dataset that should be used as the keys
of the join operation left side. The join operation is then done
only for the matches in these columns.
tolerance : int
The tolerance for inexact "on" key matching. A right row is considered
a match with a left row if ``right.on - left.on`` is in the range
``[min(0, tolerance), max(0, tolerance)]``. ``tolerance`` may be:
- negative, in which case a past-as-of-join occurs
(match iff ``tolerance <= right.on - left.on <= 0``);
- or positive, in which case a future-as-of-join occurs
(match iff ``0 <= right.on - left.on <= tolerance``);
- or zero, in which case an exact-as-of-join occurs
(match iff ``right.on == left.on``).
The tolerance is interpreted in the same units as the "on" key.
right_on : str or list[str], default None
The columns from the right_dataset that should be used as the on key
on the join operation right side.
When ``None`` use the same key name as the left dataset.
right_by : str or list[str], default None
The columns from the right_dataset that should be used as by keys
on the join operation right side.
When ``None`` use the same key names as the left dataset.
Returns
-------
InMemoryDataset Dataset.__reduce_cython__(self) Dataset.__setstate_cython__(self, __pyx_state) copy get NSt3__120__shared_ptr_emplaceIN5arrow8IteratorINS_10shared_ptrINS1_7dataset8FragmentEEEEENS_9allocatorIS7_EEEE fragment_scan_options memory_pool expression partition_expression _scan_options NSt3__120__shared_ptr_emplaceIN5arrow7dataset14ScannerBuilderENS_9allocatorIS3_EEEE values from_dataset Scanner.from_dataset(Dataset dataset, *, columns=None, filter=None, int batch_size=_DEFAULT_BATCH_SIZE, int batch_readahead=_DEFAULT_BATCH_READAHEAD, int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, FragmentScanOptions fragment_scan_options=None, bool use_threads=True, bool cache_metadata=True, MemoryPool memory_pool=None)
Create Scanner from Dataset,
Parameters
----------
dataset : Dataset
Dataset to scan.
columns : list[str] or dict[str, Expression], 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 131_072
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.
batch_readahead : int, default 16
The number of batches to read ahead in a file. This might not work
for all file formats. Increasing this number will increase
RAM usage but could also improve IO utilization.
fragment_readahead : int, default 4
The number of files to read ahead. Increasing this number will increase
RAM usage but could also improve IO utilization.
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.
use_threads : bool, default True
If enabled, then maximum parallelism will be used determined by
the number of available CPU cores.
cache_metadata : bool, default True
If enabled, metadata may be cached when scanning to speed up
repeated scans.
memory_pool : MemoryPool, default None
For memory allocations, if required. If not specified, uses the
default pool. from_fragment Scanner.from_fragment(Fragment fragment, *, Schema schema=None, columns=None, Expression filter=None, int batch_size=_DEFAULT_BATCH_SIZE, int batch_readahead=_DEFAULT_BATCH_READAHEAD, int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, FragmentScanOptions fragment_scan_options=None, bool use_threads=True, bool cache_metadata=True, MemoryPool memory_pool=None)
Create Scanner from Fragment,
Parameters
----------
fragment : Fragment
fragment to scan.
schema : Schema, optional
The schema of the fragment.
columns : list[str] or dict[str, Expression], 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 131_072
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.
batch_readahead : int, default 16
The number of batches to read ahead in a file. This might not work
for all file formats. Increasing this number will increase
RAM usage but could also improve IO utilization.
fragment_readahead : int, default 4
The number of files to read ahead. Increasing this number will increase
RAM usage but could also improve IO utilization.
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.
use_threads : bool, default True
If enabled, then maximum parallelism will be used determined by
the number of available CPU cores.
cache_metadata : bool, default True
If enabled, metadata may be cached when scanning to speed up
repeated scans.
memory_pool : MemoryPool, default None
For memory allocations, if required. If not specified, uses the
default pool. from_batches Scanner.from_batches(source, *, Schema schema=None, columns=None, Expression filter=None, int batch_size=_DEFAULT_BATCH_SIZE, int batch_readahead=_DEFAULT_BATCH_READAHEAD, int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, FragmentScanOptions fragment_scan_options=None, bool use_threads=True, bool cache_metadata=True, MemoryPool memory_pool=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 or Arrow-compatible stream object
The iterator of Batches. This can be a pyarrow RecordBatchReader,
any object that implements the Arrow PyCapsule Protocol for
streams, or an actual Python iterator of RecordBatches.
schema : Schema
The schema of the batches (required when passing a Python
iterator).
columns : list[str] or dict[str, Expression], 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 131_072
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.
batch_readahead : int, default 16
The number of batches to read ahead in a file. This might not work
for all file formats. Increasing this number will increase
RAM usage but could also improve IO utilization.
fragment_readahead : int, default 4
The number of files to read ahead. Increasing this number will increase
RAM usage but could also improve IO utilization.
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.
use_threads : bool, default True
If enabled, then maximum parallelism will be used determined by
the number of available CPU cores.
cache_metadata : bool, default True
If enabled, metadata may be cached when scanning to speed up
repeated scans.
memory_pool : MemoryPool, default None
For memory allocations, if required. If not specified, uses the
default pool. Scanner.to_batches(self)
Consume a Scanner in record batches.
Returns
-------
record_batches : iterator of RecordBatch scan_batches Scanner.scan_batches(self)
Consume a Scanner in record batches with corresponding fragments.
Returns
-------
record_batches : iterator of TaggedRecordBatch 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.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.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.count_rows(self)
Count rows matching the scanner filter.
Returns
-------
count : int to_reader Scanner.to_reader(self)
Consume this scanner as a RecordBatchReader.
Returns
-------
RecordBatchReader Scanner.__reduce_cython__(self) Scanner.__setstate_cython__(self, __pyx_state) dataset fragment NSt3__120__shared_ptr_emplaceIN5arrow7dataset11ScanOptionsENS_9allocatorIS3_EEEE _iterator FragmentScanOptions.__reduce_cython__(self) FragmentScanOptions.__setstate_cython__(self, __pyx_state) type_name 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 make_fragment FileFormat.make_fragment(self, file, filesystem=None, Expression partition_expression=None, *, file_size=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, optional
An expression that is guaranteed true for all rows in the fragment. Allows
fragment to be potentially skipped while scanning with a filter.
file_size : int, optional
The size of the file in bytes. Can improve performance with high-latency filesystems
when file size needs to be known before reading.
Returns
-------
fragment : Fragment
The file fragment make_write_options FileFormat.make_write_options(self) FileFormat.__reduce_cython__(self) FileFormat.__setstate_cython__(self, __pyx_state) default_fragment_scan_options options FileWriteOptions.__reduce_cython__(self) FileWriteOptions.__setstate_cython__(self, __pyx_state) format Fragment.scanner(self, Schema schema=None, columns=None, Expression filter=None, int batch_size=_DEFAULT_BATCH_SIZE, int batch_readahead=_DEFAULT_BATCH_READAHEAD, int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, FragmentScanOptions fragment_scan_options=None, bool use_threads=True, bool cache_metadata=True, MemoryPool memory_pool=None)
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
its Dataset's schema. If not specified this will use the
Fragment's physical schema which might differ for each 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 131_072
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.
batch_readahead : int, default 16
The number of batches to read ahead in a file. This might not work
for all file formats. Increasing this number will increase
RAM usage but could also improve IO utilization.
fragment_readahead : int, default 4
The number of files to read ahead. Increasing this number will increase
RAM usage but could also improve IO utilization.
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.
use_threads : bool, default True
If enabled, then maximum parallelism will be used determined by
the number of available CPU cores.
cache_metadata : bool, default True
If enabled, metadata may be cached when scanning to speed up
repeated scans.
memory_pool : MemoryPool, default None
For memory allocations, if required. If not specified, uses the
default pool.
Returns
-------
scanner : Scanner Fragment.to_batches(self, Schema schema=None, columns=None, Expression filter=None, int batch_size=_DEFAULT_BATCH_SIZE, int batch_readahead=_DEFAULT_BATCH_READAHEAD, int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, FragmentScanOptions fragment_scan_options=None, bool use_threads=True, bool cache_metadata=True, MemoryPool memory_pool=None)
Read the fragment as materialized record batches.
Parameters
----------
schema : Schema, optional
Concrete schema to use for scanning.
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 131_072
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.
batch_readahead : int, default 16
The number of batches to read ahead in a file. This might not work
for all file formats. Increasing this number will increase
RAM usage but could also improve IO utilization.
fragment_readahead : int, default 4
The number of files to read ahead. Increasing this number will increase
RAM usage but could also improve IO utilization.
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.
use_threads : bool, default True
If enabled, then maximum parallelism will be used determined by
the number of available CPU cores.
cache_metadata : bool, default True
If enabled, metadata may be cached when scanning to speed up
repeated scans.
memory_pool : MemoryPool, default None
For memory allocations, if required. If not specified, uses the
default pool.
Returns
-------
record_batches : iterator of RecordBatch Fragment.to_table(self, Schema schema=None, columns=None, Expression filter=None, int batch_size=_DEFAULT_BATCH_SIZE, int batch_readahead=_DEFAULT_BATCH_READAHEAD, int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, FragmentScanOptions fragment_scan_options=None, bool use_threads=True, bool cache_metadata=True, MemoryPool memory_pool=None)
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.
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 131_072
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.
batch_readahead : int, default 16
The number of batches to read ahead in a file. This might not work
for all file formats. Increasing this number will increase
RAM usage but could also improve IO utilization.
fragment_readahead : int, default 4
The number of files to read ahead. Increasing this number will increase
RAM usage but could also improve IO utilization.
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.
use_threads : bool, default True
If enabled, then maximum parallelism will be used determined by
the number of available CPU cores.
cache_metadata : bool, default True
If enabled, metadata may be cached when scanning to speed up
repeated scans.
memory_pool : MemoryPool, default None
For memory allocations, if required. If not specified, uses the
default pool.
Returns
-------
table : Table Fragment.take(self, indices, columns=None, Expression filter=None, int batch_size=_DEFAULT_BATCH_SIZE, int batch_readahead=_DEFAULT_BATCH_READAHEAD, int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, FragmentScanOptions fragment_scan_options=None, bool use_threads=True, bool cache_metadata=True, MemoryPool memory_pool=None)
Select rows of data by index.
Parameters
----------
indices : Array or array-like
The indices of row to select in the dataset.
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 131_072
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.
batch_readahead : int, default 16
The number of batches to read ahead in a file. This might not work
for all file formats. Increasing this number will increase
RAM usage but could also improve IO utilization.
fragment_readahead : int, default 4
The number of files to read ahead. Increasing this number will increase
RAM usage but could also improve IO utilization.
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.
use_threads : bool, default True
If enabled, then maximum parallelism will be used determined by
the number of available CPU cores.
cache_metadata : bool, default True
If enabled, metadata may be cached when scanning to speed up
repeated scans.
memory_pool : MemoryPool, default None
For memory allocations, if required. If not specified, uses the
default pool.
Returns
-------
Table Fragment.head(self, int num_rows, columns=None, Expression filter=None, int batch_size=_DEFAULT_BATCH_SIZE, int batch_readahead=_DEFAULT_BATCH_READAHEAD, int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, FragmentScanOptions fragment_scan_options=None, bool use_threads=True, bool cache_metadata=True, MemoryPool memory_pool=None)
Load the first N rows of the fragment.
Parameters
----------
num_rows : int
The number of rows to load.
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 131_072
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.
batch_readahead : int, default 16
The number of batches to read ahead in a file. This might not work
for all file formats. Increasing this number will increase
RAM usage but could also improve IO utilization.
fragment_readahead : int, default 4
The number of files to read ahead. Increasing this number will increase
RAM usage but could also improve IO utilization.
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.
use_threads : bool, default True
If enabled, then maximum parallelism will be used determined by
the number of available CPU cores.
cache_metadata : bool, default True
If enabled, metadata may be cached when scanning to speed up
repeated scans.
memory_pool : MemoryPool, default None
For memory allocations, if required. If not specified, uses the
default pool.
Returns
-------
Table Fragment.count_rows(self, Expression filter=None, int batch_size=_DEFAULT_BATCH_SIZE, int batch_readahead=_DEFAULT_BATCH_READAHEAD, int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, FragmentScanOptions fragment_scan_options=None, bool use_threads=True, bool cache_metadata=True, MemoryPool memory_pool=None)
Count rows matching the scanner filter.
Parameters
----------
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 131_072
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.
batch_readahead : int, default 16
The number of batches to read ahead in a file. This might not work
for all file formats. Increasing this number will increase
RAM usage but could also improve IO utilization.
fragment_readahead : int, default 4
The number of files to read ahead. Increasing this number will increase
RAM usage but could also improve IO utilization.
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.
use_threads : bool, default True
If enabled, then maximum parallelism will be used determined by
the number of available CPU cores.
cache_metadata : bool, default True
If enabled, metadata may be cached when scanning to speed up
repeated scans.
memory_pool : MemoryPool, default None
For memory allocations, if required. If not specified, uses the
default pool.
Returns
-------
count : int Fragment.__reduce_cython__(self) Fragment.__setstate_cython__(self, __pyx_state) physical_schema items FileFragment.__reduce__(self) open FileFragment.open(self)
Open a NativeFile of the buffer or file viewed by this fragment. path filesystem buffer parse Partitioning.parse(self, path)
Parse a path into a partition expression.
Parameters
----------
path : str
Returns
-------
pyarrow.dataset.Expression Partitioning.format(self, expr)
Convert a filter expression into a tuple of (directory, filename) using
the current partitioning scheme
Parameters
----------
expr : pyarrow.dataset.Expression
Returns
-------
tuple[str, str]
Examples
--------
Specify the Schema for paths like "/2009/June":
>>> import pyarrow as pa
>>> import pyarrow.dataset as ds
>>> import pyarrow.compute as pc
>>> part = ds.partitioning(pa.schema([("year", pa.int16()),
... ("month", pa.string())]))
>>> part.format(
... (pc.field("year") == 1862) & (pc.field("month") == "Jan")
... )
('1862/Jan', '') Partitioning.__reduce_cython__(self) Partitioning.__setstate_cython__(self, __pyx_state) PartitioningFactory.__reduce__(self) WrittenFile.__reduce_cython__(self) WrittenFile.__setstate_cython__(self, __pyx_state) metadata size InMemoryDataset.__reduce_cython__(self) InMemoryDataset.__setstate_cython__(self, __pyx_state) NSt3__120__shared_ptr_emplaceIN5arrow7dataset15InMemoryDatasetENS_9allocatorIS3_EEEE UnionDataset.__reduce__(self) children FileSystemDataset.__reduce__(self) from_paths FileSystemDataset.from_paths(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, CsvFileFormat, and JsonFileFormat 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. partitioning IpcFileWriteOptions.__reduce_cython__(self) IpcFileWriteOptions.__setstate_cython__(self, __pyx_state) write_options NSt3__120__shared_ptr_pointerIPN5arrow3ipc15IpcWriteOptionsENS_10shared_ptrIS3_E27__shared_ptr_default_deleteIS3_S3_EENS_9allocatorIS3_EEEE NSt3__110shared_ptrIN5arrow3ipc15IpcWriteOptionsEE27__shared_ptr_default_deleteIS3_S3_EE equals IpcFileFormat.equals(self, IpcFileFormat other)
Parameters
----------
other : pyarrow.dataset.IpcFileFormat
Returns
-------
True IpcFileFormat.make_write_options(self, **kwargs)
Parameters
----------
**kwargs : dict
Returns
-------
pyarrow.ipc.IpcWriteOptions IpcFileFormat.__reduce__(self) other NSt3__120__shared_ptr_pointerIPN5arrow7dataset13IpcFileFormatENS_10shared_ptrINS2_10FileFormatEE27__shared_ptr_default_deleteIS6_S3_EENS_9allocatorIS3_EEEE NSt3__110shared_ptrIN5arrow7dataset10FileFormatEE27__shared_ptr_default_deleteIS3_NS2_13IpcFileFormatEEE CsvFileFormat.make_write_options(self, **kwargs)
Parameters
----------
**kwargs : dict
Returns
-------
pyarrow.csv.WriteOptions CsvFileFormat.equals(self, CsvFileFormat other)
Parameters
----------
other : pyarrow.dataset.CsvFileFormat
Returns
-------
bool CsvFileFormat.__reduce__(self) parse_options _read_options_py convert_options read_options NSt3__120__shared_ptr_pointerIPN5arrow7dataset13CsvFileFormatENS_10shared_ptrINS2_10FileFormatEE27__shared_ptr_default_deleteIS6_S3_EENS_9allocatorIS3_EEEE NSt3__110shared_ptrIN5arrow7dataset10FileFormatEE27__shared_ptr_default_deleteIS3_NS2_13CsvFileFormatEEE CsvFragmentScanOptions.equals(self, CsvFragmentScanOptions other)
Parameters
----------
other : pyarrow.dataset.CsvFragmentScanOptions
Returns
-------
bool CsvFragmentScanOptions.__reduce__(self) N5arrow7dataset22CsvFragmentScanOptionsE N5arrow7dataset19FragmentScanOptionsE csv NSt3__120__shared_ptr_pointerIPN5arrow7dataset22CsvFragmentScanOptionsENS_10shared_ptrINS2_19FragmentScanOptionsEE27__shared_ptr_default_deleteIS6_S3_EENS_9allocatorIS3_EEEE NSt3__110shared_ptrIN5arrow7dataset19FragmentScanOptionsEE27__shared_ptr_default_deleteIS3_NS2_22CsvFragmentScanOptionsEEE CsvFileWriteOptions.__reduce_cython__(self) CsvFileWriteOptions.__setstate_cython__(self, __pyx_state) NSt3__120__shared_ptr_pointerIPN5arrow3csv12WriteOptionsENS_10shared_ptrIS3_E27__shared_ptr_default_deleteIS3_S3_EENS_9allocatorIS3_EEEE NSt3__110shared_ptrIN5arrow3csv12WriteOptionsEE27__shared_ptr_default_deleteIS3_S3_EE JsonFileFormat.equals(self, JsonFileFormat other)
Parameters
----------
other : pyarrow.dataset.JsonFileFormat
Returns
-------
bool JsonFileFormat.__reduce__(self) NSt3__120__shared_ptr_pointerIPN5arrow7dataset14JsonFileFormatENS_10shared_ptrINS2_10FileFormatEE27__shared_ptr_default_deleteIS6_S3_EENS_9allocatorIS3_EEEE NSt3__110shared_ptrIN5arrow7dataset10FileFormatEE27__shared_ptr_default_deleteIS3_NS2_14JsonFileFormatEEE JsonFragmentScanOptions.equals(self, JsonFragmentScanOptions other)
Parameters
----------
other : pyarrow.dataset.JsonFragmentScanOptions
Returns
-------
bool JsonFragmentScanOptions.__reduce__(self) N5arrow7dataset23JsonFragmentScanOptionsE json NSt3__120__shared_ptr_pointerIPN5arrow7dataset23JsonFragmentScanOptionsENS_10shared_ptrINS2_19FragmentScanOptionsEE27__shared_ptr_default_deleteIS6_S3_EENS_9allocatorIS3_EEEE NSt3__110shared_ptrIN5arrow7dataset19FragmentScanOptionsEE27__shared_ptr_default_deleteIS3_NS2_23JsonFragmentScanOptionsEEE KeyValuePartitioning.__reduce__(self) dictionaries discover DirectoryPartitioning.discover(field_names=None, infer_dictionary=False, max_partition_dictionary_size=0, schema=None, segment_encoding='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. NSt3__120__shared_ptr_emplaceIN5arrow7dataset21DirectoryPartitioningENS_9allocatorIS3_EEEE HivePartitioning.__reduce__(self) HivePartitioning.discover(infer_dictionary=False, max_partition_dictionary_size=0, null_fallback='__HIVE_DEFAULT_PARTITION__', schema=None, segment_encoding='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. __HIVE_DEFAULT_PARTITION__ NSt3__120__shared_ptr_emplaceIN5arrow7dataset16HivePartitioningENS_9allocatorIS3_EEEE N5arrow7dataset12PartitioningE N5arrow4util18EqualityComparableINS_7dataset12PartitioningEEE FilenamePartitioning.discover(field_names=None, infer_dictionary=False, schema=None, segment_encoding='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. NSt3__120__shared_ptr_emplaceIN5arrow7dataset20FilenamePartitioningENS_9allocatorIS3_EEEE . FileSystemFactoryOptions.__reduce_cython__(self) FileSystemFactoryOptions.__setstate_cython__(self, __pyx_state) partitioning_factory partition_base_dir exclude_invalid_files selector_ignore_prefixes value FileSystemDatasetFactory.__reduce_cython__(self) FileSystemDatasetFactory.__setstate_cython__(self, __pyx_state) UnionDatasetFactory.__reduce_cython__(self) UnionDatasetFactory.__setstate_cython__(self, __pyx_state) factories NSt3__120__shared_ptr_emplaceIN5arrow8IteratorINS_10shared_ptrINS1_11RecordBatchEEEEENS_9allocatorIS6_EEEE RecordBatchIterator.__reduce_cython__(self) RecordBatchIterator.__setstate_cython__(self, __pyx_state) NSt3__120__shared_ptr_emplaceIN5arrow8IteratorINS1_7dataset17TaggedRecordBatchEEENS_9allocatorIS5_EEEE TaggedRecordBatchIterator.__reduce_cython__(self) TaggedRecordBatchIterator.__setstate_cython__(self, __pyx_state) _set_options _ScanNodeOptions._set_options(self, Dataset dataset, dict scan_options) _ScanNodeOptions.__reduce_cython__(self) _ScanNodeOptions.__setstate_cython__(self, __pyx_state) scan_options NSt3__120__shared_ptr_pointerIPN5arrow7dataset15ScanNodeOptionsENS_10shared_ptrINS1_5acero15ExecNodeOptionsEE27__shared_ptr_default_deleteIS7_S3_EENS_9allocatorIS3_EEEE NSt3__110shared_ptrIN5arrow5acero15ExecNodeOptionsEE27__shared_ptr_default_deleteIS3_NS1_7dataset15ScanNodeOptionsEEE N5arrow7dataset15ScanNodeOptionsE N5arrow5acero15ExecNodeOptionsE type DictionaryType Array Table RecordBatch RecordBatchReader pyarrow._compute _get_orc_fileformat _get_orc_fileformat()
Import OrcFileFormat on first usage (to avoid circular import issue
when `pyarrow._dataset_orc` would be imported first) _get_parquet_classes _get_parquet_classes()
Import Parquet class files on first usage (to avoid circular import issue
when `pyarrow._dataset_parquet` would be imported first) _get_parquet_symbol _get_parquet_symbol(name)
Get a symbol from pyarrow.parquet if the latter is importable, otherwise
return None. _constructor_directory_partitioning_factory _constructor_directory_partitioning_factory(*args) _constructor_hive_partitioning_factory _constructor_hive_partitioning_factory(*args) _constructor_filename_partitioning_factory _constructor_filename_partitioning_factory(*args) get_partition_keys 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.
Parameters
----------
partition_expression : pyarrow.dataset.Expression
Returns
-------
dict
Examples
--------
For example, an expression of
<pyarrow.dataset.Expression ((part == A:string) and (year == 2016:int32))>
is converted to {'part': 'A', 'year': 2016} _filesystemdataset_write _filesystemdataset_write(Scanner data, base_dir, str basename_template, FileSystem filesystem, Partitioning partitioning, bool preserve_order, FileWriteOptions file_options, int max_partitions, file_visitor, str 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 data base_dir basename_template file_options existing_data_behavior NSt3__110__function6__funcIN5arrow7dataset29FileSystemDatasetWriteOptions17writer_pre_finishMUlPNS3_10FileWriterEE_ENS_9allocatorIS7_EEFNS2_6StatusES6_EEE NSt3__110__function6__baseIFN5arrow6StatusEPNS2_7dataset10FileWriterEEEE N5arrow7dataset29FileSystemDatasetWriteOptions17writer_pre_finishMUlPNS0_10FileWriterEE_E NSt3__110__function6__funcIN5arrow7dataset29FileSystemDatasetWriteOptions18writer_post_finishMUlPNS3_10FileWriterEE_ENS_9allocatorIS7_EEFNS2_6StatusES6_EEE N5arrow7dataset29FileSystemDatasetWriteOptions18writer_post_finishMUlPNS0_10FileWriterEE_E NSt3__120__shared_ptr_emplaceIN5arrow2py13BoundFunctionIFvP7_objectPNS1_7dataset10FileWriterEEEENS_9allocatorISA_EEEE NSt3__110__function6__funcIZN5arrow2py12BindFunctionIFNS2_6StatusEPNS2_7dataset10FileWriterEEvJS8_EEENS_8functionIT_EEPFT0_P7_objectDpT1_ESF_EUlS8_E_NS_9allocatorISK_EES9_EE ZN5arrow2py12BindFunctionIFNS_6StatusEPNS_7dataset10FileWriterEEvJS5_EEENSt3__18functionIT_EEPFT0_P7_objectDpT1_ESD_EUlS5_E_ ScanNodeOptions.__init__(self, Dataset dataset, **kwargs) __pyx_unpickle_WrittenFile __pyx_unpickle_WrittenFile(__pyx_type, long __pyx_checksum, __pyx_state) _dataset loader __loader__ origin __file__ parent __package__ submodule_search_locations __path__ Interpreter change detected - this module can only be loaded into one interpreter per process. Module '_dataset' has already been imported. Re-initialisation is not supported. builtins cython_runtime __builtins__ __orig_bases__ init pyarrow._dataset pyarrow/_dataset.pxd compile time Python version %d.%d of module '%.100s' %s runtime version %d.%d does not match _cython_3_1_3amsendbackport Shared Cython type %.200s is not a type object Shared Cython type %.200s has the wrong size, try recompiling _cython_3_1_3amsendbackport._common_types_metatype _cython_3_1_3amsendbackport.cython_function_or_method <cyfunction %U at %p> unbound method %.200S() needs an argument keywords must be strings takes no arguments takes exactly one argument Bad call flags for CyFunction takes no keyword arguments %.200s() %s (%zd given) %.200s() %s __dictoffset__ __vectorcalloffset__ __weaklistoffset__ func_doc func_name func_dict func_globals __globals__ func_closure __closure__ func_code __code__ func_defaults __defaults__ __kwdefaults__ __annotations__ __name__ must be set to a string object __qualname__ must be set to a string object function's dictionary may not be deleted setting function's dictionary to a non-dict __defaults__ must be set to a tuple object changes to cyfunction.__defaults__ will not currently affect the values used in function calls __kwdefaults__ must be set to a dict object changes to cyfunction.__kwdefaults__ will not currently affect the values used in function calls __annotations__ must be set to a dict object _cython_3_1_3amsendbackport.generator generator already executing send(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. generator ignored GeneratorExit while calling a Python object NULL result without error in PyObject_Call raise: arg 3 must be a traceback or None instance exception may not have a separate value calling %R should have returned an instance of BaseException, not %R raise: exception class must be a subclass of BaseException cannot pickle '%.200s' object gi_yieldfrom object being iterated by 'yield from', or None gi_code name of the generator qualified name of the generator gi_frame Frame of the generator gi_running _make_file_source arrow::dataset::FileSource (PyObject *, struct __pyx_opt_args_7pyarrow_8_dataset__make_file_source *__pyx_optional_args) __pyx_capi__ pyarrow._dataset._make_file_source Called Open() on an uninitialized FileSource basic_string Constructed with a non-error status: string.from_py.__pyx_convert_string_from_py_6libcpp_6string_std__in_string Unknown exception an integer is required __int__ returned non-int (type %.200s). The ability to return an instance of a strict subclass of int is deprecated, and may be removed in a future version of Python. __int__ returned non-int (type %.200s) string.to_py.__pyx_convert_PyBytes_string_to_py_6libcpp_6string_std__in_string hasattr(): attribute name must be string _Weakrefable pyarrow._acero ExecNodeOptions %.200s.%.200s is not a type object %.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject pyarrow._dataset.DatasetFactory.wrap pyarrow._dataset.DatasetFactory DatasetFactory()
DatasetFactory is used to create a Dataset, inspect the Schema
of the fragments contained in it, and declare a partitioning. at least at most exactly %.200s() takes %.8s %zd positional argument%.1s (%zd given) %s() got an unexpected keyword argument '%U' pyarrow._dataset.DatasetFactory.inspect_schemas vector pyarrow._dataset.DatasetFactory.inspect %s() got multiple values for keyword argument '%U' %.200s() keywords must be strings str Expected %s, got %.200s pyarrow._dataset.DatasetFactory.finish Missing type object Argument '%.200s' has incorrect type (expected %.200s, got %.200s)%s%U pyarrow._dataset.DatasetFactory.__reduce_cython__ pyarrow._dataset.DatasetFactory.__setstate_cython__ pyarrow._dataset.DatasetFactory.root_partition.__get__ __del__ pyarrow._dataset.DatasetFactory.root_partition.__set__ pyarrow._dataset.DatasetFactory.__init__ base class '%.200s' is not a heap type extension type '%.200s' has no __dict__ slot, but base type '%.200s' has: either add 'cdef dict __dict__' to the extension type or add '__slots__ = [...]' to the base type multiple bases have vtable conflict: '%.200s' and '%.200s' Unable to initialize pickling for %.200s pyarrow._dataset.Dataset.init pyarrow._dataset.Dataset.wrap pyarrow._dataset.Dataset Dataset()
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). pyarrow._dataset.Dataset.replace_schema 'NoneType' object has no attribute '%.30s' pyarrow._dataset.Dataset.get_fragments pyarrow._dataset.Dataset._get_fragments can't send non-None value to a just-started generator generator raised StopIteration pyarrow._dataset.Dataset._scanner_options pyarrow._dataset.Dataset.scanner pyarrow._dataset.Dataset.to_batches pyarrow._dataset.Dataset.to_table pyarrow._dataset.Dataset.take pyarrow._dataset.Dataset.head pyarrow._dataset.Dataset.count_rows pyarrow._dataset.Dataset.filter Argument '%.200s' must not be None pyarrow._dataset.Dataset.sort_by function dictionary changed size during iteration 'NoneType' object is not iterable need more than %zd value%.1s to unpack too many values to unpack (expected %zd) pyarrow._dataset.Dataset.join pyarrow._dataset.Dataset.join_asof pyarrow._dataset.Dataset.__reduce_cython__ pyarrow._dataset.Dataset.__setstate_cython__
An Expression which evaluates to true for all data viewed by this
Dataset.
The common schema of the full Dataset _scan_options: dict pyarrow._dataset.Dataset.partition_expression.__get__ pyarrow._dataset.Dataset.schema.__get__ dict pyarrow._dataset.Dataset._scan_options.__set__ pyarrow._dataset.Dataset.__init__ pyarrow._dataset.Scanner.wrap pyarrow._dataset.Scanner._make_scan_options pyarrow._dataset._populate_builder join() result is too long for a Python string vector.from_py.__pyx_convert_vector_from_py_std_3a__3a_string pyarrow._dataset.Scanner Scanner()
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. pyarrow._dataset.Scanner.from_dataset pyarrow._dataset.Scanner.from_fragment pyarrow._dataset.Scanner.from_batches pyarrow._dataset.Scanner.to_batches pyarrow._dataset.Scanner.to_batches._iterator pyarrow._dataset.Scanner.scan_batches pyarrow._dataset.Scanner.to_table pyarrow._dataset.Scanner.take pyarrow._dataset.Scanner.head pyarrow._dataset.Scanner.count_rows pyarrow._dataset.Scanner.to_reader pyarrow._dataset.Scanner.__reduce_cython__ pyarrow._dataset.Scanner.__setstate_cython__ dataset_schema The 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.
pyarrow._dataset.Scanner.dataset_schema.__get__ pyarrow._dataset.Scanner.projected_schema.__get__ pyarrow._dataset.Scanner.__init__ pyarrow._dataset.FragmentScanOptions.wrap pyarrow._dataset.FragmentScanOptions FragmentScanOptions()
Scan options specific to a particular fragment and scan operation. pyarrow._dataset.FragmentScanOptions.__eq__ pyarrow._dataset.FragmentScanOptions.__reduce_cython__ pyarrow._dataset.FragmentScanOptions.__setstate_cython__ pyarrow._dataset.FragmentScanOptions.type_name.__get__ pyarrow._dataset.FragmentScanOptions.__init__ pyarrow._dataset.FileFormat.wrap pyarrow._dataset.FileFormat._set_default_fragment_scan_options pyarrow._dataset.FileFormat._finish_write pyarrow._dataset.FileFormat FileFormat() pyarrow._dataset.FileFormat.__eq__ pyarrow._dataset.FileFormat.inspect pyarrow._dataset.FileFormat.make_fragment pyarrow._dataset.FileFormat.make_write_options pyarrow._dataset.FileFormat.__reduce_cython__ pyarrow._dataset.FileFormat.__setstate_cython__ default_extname pyarrow._dataset.FileFormat.default_extname.__get__ pyarrow._dataset.FileFormat.default_fragment_scan_options.__get__ pyarrow._dataset.FileFormat.default_fragment_scan_options.__set__ pyarrow._dataset.FileFormat.__init__ pyarrow._dataset.FileWriteOptions.wrap pyarrow._dataset.FileWriteOptions FileWriteOptions() pyarrow._dataset.FileWriteOptions.__reduce_cython__ pyarrow._dataset.FileWriteOptions.__setstate_cython__ pyarrow._dataset.FileWriteOptions.format.__get__ pyarrow._dataset.FileWriteOptions.__init__ pyarrow._dataset.Fragment.wrap pyarrow._dataset.Fragment Fragment()
Fragment of data from a Dataset. pyarrow._dataset.Fragment.scanner pyarrow._dataset.Fragment.to_batches pyarrow._dataset.Fragment.to_table pyarrow._dataset.Fragment.take pyarrow._dataset.Fragment.head pyarrow._dataset.Fragment.count_rows pyarrow._dataset.Fragment.__reduce_cython__ pyarrow._dataset.Fragment.__setstate_cython__ Return 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.Fragment.physical_schema.__get__ pyarrow._dataset.Fragment.partition_expression.__get__ pyarrow._dataset.Fragment.__init__ pyarrow._dataset.FileFragment.init pyarrow._dataset.FileFragment A Fragment representing a data file. pyarrow._dataset.FileFragment.__repr__ pyarrow._dataset.FileFragment.__reduce__ pyarrow._dataset.FileFragment.open <Buffer>
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.
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.FileFragment.path.__get__ pyarrow._dataset.FileFragment.filesystem.__get__ pyarrow._dataset.FileFragment.buffer.__get__ pyarrow._dataset.FileFragment.format.__get__ pyarrow._dataset.Partitioning.wrap pyarrow._dataset.Partitioning Partitioning() pyarrow._dataset.Partitioning.__eq__ pyarrow._dataset.Partitioning.parse pyarrow._dataset.Partitioning.format pyarrow._dataset.Partitioning.__reduce_cython__ pyarrow._dataset.Partitioning.__setstate_cython__ The arrow Schema attached to the partitioning. pyarrow._dataset.Partitioning.schema.__get__ pyarrow._dataset.Partitioning.__init__ pyarrow._dataset.PartitioningFactory.wrap pyarrow._dataset.PartitioningFactory PartitioningFactory() pyarrow._dataset.PartitioningFactory.__reduce__ pyarrow._dataset.PartitioningFactory.type_name.__get__ pyarrow._dataset.PartitioningFactory.__init__ pyarrow._dataset.WrittenFile WrittenFile(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. pyarrow._dataset.WrittenFile.__reduce_cython__ pyarrow._dataset.WrittenFile.__setstate_cython__ tuple 'NoneType' object is not subscriptable pyarrow._dataset.__pyx_unpickle_WrittenFile__set_state path: str metadata: object size: 'int64_t' pyarrow._dataset.WrittenFile.path.__set__ pyarrow._dataset.WrittenFile.size.__get__ pyarrow._dataset.WrittenFile.size.__set__ pyarrow._dataset.WrittenFile.__init__ pyarrow._dataset.InMemoryDataset.init pyarrow._dataset.InMemoryDataset InMemoryDataset(source, Schema schema=None)
A Dataset wrapping in-memory data.
Parameters
----------
source : RecordBatch, Table, list, tuple
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.InMemoryDataset.__reduce_cython__ pyarrow._dataset.InMemoryDataset.__setstate_cython__ pyarrow._dataset.InMemoryDataset.__init__ pyarrow._dataset.UnionDataset.init pyarrow._dataset.UnionDataset UnionDataset(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 pyarrow._dataset.UnionDataset.__reduce__ pyarrow._dataset.UnionDataset.children.__get__ pyarrow._dataset.UnionDataset.__init__ pyarrow._dataset.FileSystemDataset.init pyarrow._dataset.FileSystemDataset FileSystemDataset(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, CsvFileFormat, and JsonFileFormat are supported.
filesystem : FileSystem
FileSystem of the fragments.
root_partition : Expression, optional
The top-level partition of the DataDataset. pyarrow._dataset.FileSystemDataset.__reduce__ pyarrow._dataset.FileSystemDataset.from_paths cannot fit '%.200s' into an index-sized integer '%.200s' object is not subscriptable
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.
files List of the files The FileFormat of this source. pyarrow._dataset.FileSystemDataset.filesystem.__get__ pyarrow._dataset.FileSystemDataset.partitioning.__get__ pyarrow._dataset.FileSystemDataset.files.__get__ pyarrow._dataset.FileSystemDataset.format.__get__ pyarrow._dataset.FileSystemDataset.__init__ pyarrow._dataset.IpcFileWriteOptions.init pyarrow._dataset.IpcFileWriteOptions IpcFileWriteOptions() pyarrow._dataset.IpcFileWriteOptions.__reduce_cython__ pyarrow._dataset.IpcFileWriteOptions.__setstate_cython__ pyarrow._dataset.IpcFileWriteOptions.write_options.__get__ pyarrow._dataset.IpcFileWriteOptions.__init__ pyarrow._dataset.IpcFileFormat IpcFileFormat() pyarrow._dataset.IpcFileFormat.equals pyarrow._dataset.IpcFileFormat.make_write_options pyarrow._dataset.IpcFileFormat.__reduce__ pyarrow._dataset.IpcFileFormat.__init__ pyarrow._dataset.FeatherFileFormat pyarrow._dataset.CsvFileFormat.init pyarrow._dataset.CsvFileFormat._set_default_fragment_scan_options pyarrow._dataset.CsvFileFormat CsvFileFormat(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.
default_fragment_scan_options : CsvFragmentScanOptions
Default options for fragments scan.
convert_options : pyarrow.csv.ConvertOptions
Options regarding value conversion.
read_options : pyarrow.csv.ReadOptions
General read options. pyarrow._dataset.CsvFileFormat.__repr__ pyarrow._dataset.CsvFileFormat.make_write_options pyarrow._dataset.CsvFileFormat.equals pyarrow._dataset.CsvFileFormat.__reduce__ _read_options_py: pyarrow._csv.ReadOptions pyarrow._dataset.CsvFileFormat.parse_options.__get__ pyarrow._dataset.CsvFileFormat._read_options_py.__set__ pyarrow._dataset.CsvFileFormat.__init__ argument after ** must be a mapping, not NoneType pyarrow._dataset.CsvFragmentScanOptions.init pyarrow._dataset.CsvFragmentScanOptions CsvFragmentScanOptions(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.CsvFragmentScanOptions.equals pyarrow._dataset.CsvFragmentScanOptions.__reduce__ pyarrow._dataset.CsvFragmentScanOptions.convert_options.__get__ pyarrow._dataset.CsvFragmentScanOptions.read_options.__get__ pyarrow._dataset.CsvFragmentScanOptions.read_options.__set__ pyarrow._dataset.CsvFragmentScanOptions.__init__ pyarrow._dataset.CsvFileWriteOptions.init pyarrow._dataset.CsvFileWriteOptions CsvFileWriteOptions() pyarrow._dataset.CsvFileWriteOptions.__reduce_cython__ pyarrow._dataset.CsvFileWriteOptions.__setstate_cython__ pyarrow._dataset.CsvFileWriteOptions.write_options.__get__ pyarrow._dataset.CsvFileWriteOptions.__init__ pyarrow._dataset.JsonFileFormat.init pyarrow._dataset.JsonFileFormat._set_default_fragment_scan_options pyarrow._dataset.JsonFileFormat JsonFileFormat(default_fragment_scan_options=None, ParseOptions parse_options=None, ReadOptions read_options=None)
FileFormat for JSON files.
Parameters
----------
default_fragment_scan_options : JsonFragmentScanOptions
Default options for fragments scan.
parse_options : pyarrow.json.ParseOptions
Options regarding json parsing.
read_options : pyarrow.json.ReadOptions
General read options. pyarrow._dataset.JsonFileFormat.equals pyarrow._dataset.JsonFileFormat.__reduce__ pyarrow._dataset.JsonFileFormat.__init__ pyarrow._dataset.JsonFragmentScanOptions.init pyarrow._dataset.JsonFragmentScanOptions JsonFragmentScanOptions(ParseOptions parse_options=None, ReadOptions read_options=None)
Scan-specific options for JSON fragments.
Parameters
----------
parse_options : pyarrow.json.ParseOptions
Options regarding JSON parsing.
read_options : pyarrow.json.ReadOptions
General read options. pyarrow._dataset.JsonFragmentScanOptions.equals pyarrow._dataset.JsonFragmentScanOptions.__reduce__ pyarrow._dataset.JsonFragmentScanOptions.parse_options.__get__ pyarrow._dataset.JsonFragmentScanOptions.read_options.__get__ pyarrow._dataset.JsonFragmentScanOptions.__init__ pyarrow._dataset.KeyValuePartitioning KeyValuePartitioning() pyarrow._dataset.KeyValuePartitioning.__reduce__ pyarrow._dataset._wrap_segment_encoding
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.KeyValuePartitioning.dictionaries.__get__ pyarrow._dataset.KeyValuePartitioning.__init__ pyarrow._dataset.DirectoryPartitioning DirectoryPartitioning(Schema schema, dictionaries=None, segment_encoding='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.DirectoryPartitioning.discover pyarrow._dataset._get_segment_encoding pyarrow._dataset.DirectoryPartitioning.__init__ pyarrow._dataset._partitioning_dictionaries pyarrow._dataset.HivePartitioning HivePartitioning(Schema schema, dictionaries=None, null_fallback='__HIVE_DEFAULT_PARTITION__', segment_encoding='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.HivePartitioning.__reduce__ pyarrow._dataset.HivePartitioning.discover pyarrow._dataset.HivePartitioning.__init__ pyarrow._dataset.FilenamePartitioning FilenamePartitioning(Schema schema, dictionaries=None, segment_encoding='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.FilenamePartitioning.discover pyarrow._dataset.FilenamePartitioning.__init__ _ pyarrow._dataset.FileSystemFactoryOptions FileSystemFactoryOptions(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 ['.', '_']. pyarrow._dataset.FileSystemFactoryOptions.__reduce_cython__ pyarrow._dataset.FileSystemFactoryOptions.__setstate_cython__ Partitioning to apply to discovered files.
NOTE: setting this property will overwrite partitioning_factory.
PartitioningFactory to apply to discovered files and
discover a Partitioning.
NOTE: setting this property will overwrite partitioning.
Base directory to strip paths before applying the partitioning.
Whether to exclude invalid files.
List of prefixes. Files matching one of those prefixes will be
ignored by the discovery process.
pyarrow._dataset.FileSystemFactoryOptions.partitioning.__get__ pyarrow._dataset.FileSystemFactoryOptions.partitioning.__set__ pyarrow._dataset.FileSystemFactoryOptions.partitioning_factory.__get__ pyarrow._dataset.FileSystemFactoryOptions.partitioning_factory.__set__ pyarrow._dataset.FileSystemFactoryOptions.partition_base_dir.__get__ pyarrow._dataset.FileSystemFactoryOptions.partition_base_dir.__set__ pyarrow._dataset.FileSystemFactoryOptions.exclude_invalid_files.__set__ pyarrow._dataset.FileSystemFactoryOptions.selector_ignore_prefixes.__get__ pyarrow._dataset.FileSystemFactoryOptions.selector_ignore_prefixes.__set__ pyarrow._dataset.FileSystemFactoryOptions.__init__ pyarrow._dataset.FileSystemDatasetFactory FileSystemDatasetFactory(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.FileSystemDatasetFactory.__reduce_cython__ pyarrow._dataset.FileSystemDatasetFactory.__setstate_cython__ pyarrow._dataset.FileSystemDatasetFactory.__init__ pyarrow._dataset.unwrap_finfos pyarrow._dataset.UnionDatasetFactory UnionDatasetFactory(list factories)
Provides a way to inspect/discover a Dataset's expected schema before
materialization.
Parameters
----------
factories : list of DatasetFactory pyarrow._dataset.UnionDatasetFactory.__reduce_cython__ pyarrow._dataset.UnionDatasetFactory.__setstate_cython__ pyarrow._dataset.UnionDatasetFactory.__init__ pyarrow._dataset.RecordBatchIterator.wrap pyarrow._dataset.RecordBatchIterator RecordBatchIterator()
An iterator over a sequence of record batches. pyarrow._dataset.RecordBatchIterator.__next__ __next__ pyarrow._dataset.RecordBatchIterator.__reduce_cython__ pyarrow._dataset.RecordBatchIterator.__setstate_cython__ pyarrow._dataset.RecordBatchIterator.__init__ pyarrow._dataset.TaggedRecordBatchIterator.wrap pyarrow._dataset.TaggedRecordBatchIterator TaggedRecordBatchIterator()
An iterator over a sequence of record batches with fragments. pyarrow._dataset.TaggedRecordBatchIterator.__next__ pyarrow._dataset.TaggedRecordBatchIterator.__reduce_cython__ pyarrow._dataset.TaggedRecordBatchIterator.__setstate_cython__ pyarrow._dataset.TaggedRecordBatchIterator.__init__ invalid vtable found for imported type pyarrow._dataset._ScanNodeOptions pyarrow._dataset._ScanNodeOptions._set_options pyarrow._dataset._ScanNodeOptions.__reduce_cython__ pyarrow._dataset._ScanNodeOptions.__setstate_cython__ pyarrow._dataset.__pyx_scope_struct___get_fragments pyarrow._dataset.__pyx_scope_struct_1__iterator bool complex datetime date time timedelta tzinfo IpcWriteOptions IpcReadOptions Message MemoryPool DataType ListType LargeListType ListViewType LargeListViewType MapType FixedSizeListType StructType DictionaryMemo TimestampType Time32Type Time64Type DurationType FixedSizeBinaryType Decimal32Type Decimal64Type Decimal128Type Decimal256Type RunEndEncodedType BaseExtensionType ExtensionType FixedShapeTensorType Bool8Type OpaqueType UuidType JsonType _Metadata KeyValueMetadata Field Schema Scalar ArrayStatistics _PandasConvertible Tensor SparseCSRMatrix SparseCSCMatrix SparseCOOTensor SparseCSFTensor NullArray BooleanArray NumericArray IntegerArray FloatingPointArray Int8Array UInt8Array Int16Array UInt16Array Int32Array UInt32Array Int64Array UInt64Array HalfFloatArray FloatArray DoubleArray FixedSizeBinaryArray Decimal32Array Decimal64Array Decimal128Array Decimal256Array StructArray BaseListArray ListArray LargeListArray ListViewArray LargeListViewArray MapArray FixedSizeListArray UnionArray StringArray BinaryArray StringViewArray BinaryViewArray DictionaryArray ExtensionArray MonthDayNanoIntervalArray ChunkedArray _Tabular Device MemoryManager Buffer ResizableBuffer NativeFile BufferedInputStream BufferedOutputStream CompressedInputStream CompressedOutputStream _CRecordBatchWriter CacheOptions Codec StopToken pyarrow._fs FileInfo FileSelector FileSystem LocalFileSystem SubTreeFileSystem _MockFileSystem PyFileSystem Declaration UdfContext FunctionOptions _SortOptions Expression pyarrow._csv ConvertOptions ParseOptions ReadOptions WriteOptions pyarrow._json _true arrow::compute::Expression %.200s does not export expected C variable %.200s C variable %.200s.%.200s has wrong signature (expected %.500s, got %.500s) check_status int (arrow::Status const &) maybe_unbox_memory_pool arrow::MemoryPool *(struct __pyx_obj_7pyarrow_3lib_MemoryPool *) get_native_file struct __pyx_obj_7pyarrow_3lib_NativeFile *(PyObject *, bool) make_streamwrap_func std::shared_ptr<std::function<__pyx_t_7pyarrow_8includes_8libarrow_StreamWrapFunc> > (PyObject *, PyObject *) _parse_field_merge_options arrow::Field::MergeOptions (PyObject *) pyarrow_wrap_buffer PyObject *(std::shared_ptr< arrow::Buffer> const &) pyarrow_wrap_schema PyObject *(std::shared_ptr< arrow::Schema> const &) pyarrow_wrap_scalar PyObject *(std::shared_ptr< arrow::Scalar> const &) pyarrow_wrap_array PyObject *(std::shared_ptr< arrow::Array> const &) pyarrow_wrap_batch PyObject *(std::shared_ptr< arrow::RecordBatch> const &) pyarrow_wrap_table PyObject *(std::shared_ptr< arrow::Table> const &) pyarrow_unwrap_buffer std::shared_ptr< arrow::Buffer> (PyObject *) pyarrow_unwrap_schema std::shared_ptr< arrow::Schema> (PyObject *) pyarrow_unwrap_array std::shared_ptr< arrow::Array> (PyObject *) pyarrow_unwrap_table std::shared_ptr< arrow::Table> (PyObject *) _bind arrow::compute::Expression (struct __pyx_obj_7pyarrow_8_compute_Expression *, struct __pyx_obj_7pyarrow_3lib_Schema *) %.200s does not export expected C function %.200s C function %.200s.%.200s has wrong signature (expected %.500s, got %.500s) No module named '%U' cannot import name %S needs an argument pyarrow._dataset._get_orc_fileformat pyarrow._dataset._get_parquet_classes pyarrow._dataset._get_parquet_symbol Cannot convert %.200s to %.200s name '%U' is not defined value too large to convert to int pyarrow._dataset._constructor_directory_partitioning_factory pyarrow._dataset._constructor_hive_partitioning_factory pyarrow._dataset._constructor_filename_partitioning_factory __mro_entries__ must return a tuple metaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its bases pyarrow._dataset.get_partition_keys pyarrow._dataset._filesystemdataset_write pyarrow._dataset._filesystemdataset_write_visitor pyarrow._dataset.ScanNodeOptions.__init__ pyarrow._dataset.__pyx_unpickle_WrittenFile L P ! ÑXÑXQa ! Q QaQ Q QÐ
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