pyarrow.dataset.Dataset

class pyarrow.dataset.Dataset

Bases: pyarrow.lib._Weakrefable

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).

__init__(*args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(*args, **kwargs)	Initialize self.
get_fragments	Returns an iterator over the fragments in this dataset.
replace_schema	Return a copy of this Dataset with a different schema.
scan	Builds a scan operation against the dataset.
to_batches	Read the dataset as materialized record batches.
to_table	Read the dataset to an arrow table.

Attributes

partition_expression	An Expression which evaluates to true for all data viewed by this Dataset.
schema	The common schema of the full Dataset

get_fragments()

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)

partition_expression

An Expression which evaluates to true for all data viewed by this Dataset.

replace_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.

scan()

Builds a scan operation against the dataset.

It produces a stream of ScanTasks which is meant to be a unit of work to be dispatched. The tasks are not executed automatically, the user is responsible to execute and dispatch the individual tasks, so custom local task scheduling can be implemented.

Parameters

• columns (list of str, default None) – List of columns to project. Order and duplicates will be preserved. 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 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_threads (bool, default True) – If enabled, then maximum parallelism will be used determined by the number of available CPU cores.

• memory_pool (MemoryPool, default None) – For memory allocations, if required. If not specified, uses the default pool.

Returns

scan_tasks (iterator of ScanTask)

schema

The common schema of the full Dataset

to_batches()

Read the dataset as materialized record batches.

Builds a scan operation against the dataset and sequentially executes the ScanTasks as the returned generator gets consumed.

See scan method parameters documentation.

Returns

record_batches (iterator of RecordBatch)

to_table()

Read the dataset to an arrow table.

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

See scan method parameters documentation.

Returns

table (Table instance)

pyarrow.dataset.HivePartitioning pyarrow.dataset.FileSystemDataset