pyarrow.dataset.Scanner¶

class pyarrow.dataset.Scanner¶

Bases: pyarrow.lib._Weakrefable

A materialized scan operation with context and options bound.

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

Parameters

datasetDataset: Dataset to scan.
columnslist of str or dict, default None: The columns to project. This can be a list of column names to include (order and duplicates will be preserved), or a dictionary with {new_column_name: expression} values for more advanced projections. The 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.
filterExpression, 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_sizeint, default 1M: The maximum row count for scanned record batches. If scanned record batches are overflowing memory then this method can be called to reduce their size.
use_threadsbool, default True: If enabled, then maximum parallelism will be used determined by the number of available CPU cores.
use_asyncbool, default True: This flag is deprecated and is being kept for this release for backwards compatibility. It will be removed in the next release.
memory_poolMemoryPool, default None: For memory allocations, if required. If not specified, uses the default pool.

__init__(*args, **kwargs)¶

Methods

`__init__`(args, *kwargs)
`count_rows`(self)	Count rows matching the scanner filter.
`from_batches`(source, Schema schema=None, ...)	Create a Scanner from an iterator of batches.
`from_dataset`(Dataset dataset, ...[, ...])	Create Scanner from Dataset, refer to Scanner class doc for additional details on Scanner.
`from_fragment`(Fragment fragment, ...[, ...])	Create Scanner from Fragment, refer to Scanner class doc for additional details on Scanner.
`head`(self, int num_rows)	Load the first N rows of the dataset.
`scan_batches`(self)	Consume a Scanner in record batches with corresponding fragments.
`take`(self, indices)	Select rows of data by index.
`to_batches`(self)	Consume a Scanner in record batches.
`to_reader`(self)	Consume this scanner as a RecordBatchReader.
`to_table`(self)	Convert a Scanner into a Table.

Attributes

`dataset_schema`	The schema with which batches will be read from fragments.
`projected_schema`	The materialized schema of the data, accounting for projections.

count_rows(self)¶

Count rows matching the scanner filter.

Returns

countint

dataset_schema¶: The schema with which batches will be read from fragments.

static from_batches(source, Schema schema=None, bool use_threads=True, use_async=None, MemoryPool memory_pool=None, columns=None, Expression filter=None, int batch_size=_DEFAULT_BATCH_SIZE, FragmentScanOptions fragment_scan_options=None)¶

Create a Scanner from an iterator of batches.

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

Parameters

sourceIterator: The iterator of Batches.
schemaSchema: The schema of the batches.
columnslist of str or dict, default None: The columns to project.
filterExpression, default None: Scan will return only the rows matching the filter.
batch_sizeint, default 1M: The maximum row count for scanned record batches.
use_threadsbool, default True: If enabled, then maximum parallelism will be used determined by the number of available CPU cores.
use_asyncbool, default True: This flag is deprecated and is being kept for this release for backwards compatibility. It will be removed in the next release.
memory_poolMemoryPool, default None: For memory allocations, if required. If not specified, uses the default pool.
fragment_scan_optionsFragmentScanOptions: The fragment scan options.

static from_dataset(Dataset dataset, bool use_threads=True, use_async=None, MemoryPool memory_pool=None, columns=None, Expression filter=None, int batch_size=_DEFAULT_BATCH_SIZE, FragmentScanOptions fragment_scan_options=None)¶

Create Scanner from Dataset, refer to Scanner class doc for additional details on Scanner.

Parameters

datasetDataset: Dataset to scan.
columnslist of str or dict, default None: The columns to project.
filterExpression, default None: Scan will return only the rows matching the filter.
batch_sizeint, default 1M: The maximum row count for scanned record batches.
use_threadsbool, default True: If enabled, then maximum parallelism will be used determined by the number of available CPU cores.
use_asyncbool, default N/A: This flag is deprecated and is being kept for this release for backwards compatibility. It will be removed in the next release.
memory_poolMemoryPool, default None: For memory allocations, if required. If not specified, uses the default pool.
fragment_scan_optionsFragmentScanOptions: The fragment scan options.

static from_fragment(Fragment fragment, Schema schema=None, bool use_threads=True, use_async=None, MemoryPool memory_pool=None, columns=None, Expression filter=None, int batch_size=_DEFAULT_BATCH_SIZE, FragmentScanOptions fragment_scan_options=None)¶

Create Scanner from Fragment, refer to Scanner class doc for additional details on Scanner.

Parameters

fragmentFragment: fragment to scan.
schemaSchema: The schema of the fragment.
columnslist of str or dict, default None: The columns to project.
filterExpression, default None: Scan will return only the rows matching the filter.
batch_sizeint, default 1M: The maximum row count for scanned record batches.
use_threadsbool, default True: If enabled, then maximum parallelism will be used determined by the number of available CPU cores.
use_asyncbool, default N/A: This flag is deprecated and is being kept for this release for backwards compatibility. It will be removed in the next release.
memory_poolMemoryPool, default None: For memory allocations, if required. If not specified, uses the default pool.
fragment_scan_optionsFragmentScanOptions: The fragment scan options.

head(self, int num_rows)¶

Load the first N rows of the dataset.

Returns

Table

projected_schema¶

The materialized schema of the data, accounting for projections.

This is the schema of any data returned from the scanner.

scan_batches(self)¶

Consume a Scanner in record batches with corresponding fragments.

Returns

record_batchesiterator of TaggedRecordBatch

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).

Returns

Table

to_batches(self)¶

Consume a Scanner in record batches.

Returns

record_batchesiterator of RecordBatch

to_reader(self)¶: Consume this scanner as a RecordBatchReader.

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

pyarrow.dataset.FragmentScanOptions

pyarrow.dataset.Expression