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)#

Methods

`__init__`(args, *kwargs)
`count_rows`(self, **kwargs)	Count rows matching the scanner filter.
`get_fragments`(self, Expression filter=None)	Returns an iterator over the fragments in this dataset.
`head`(self, int num_rows, **kwargs)	Load the first N rows of the dataset.
`join`(self, right_dataset, keys[, ...])	Perform a join between this dataset and another one.
`replace_schema`(self, Schema schema)	Return a copy of this Dataset with a different schema.
`scanner`(self, **kwargs)	Build a scan operation against the dataset.
`take`(self, indices, **kwargs)	Select rows of data by index.
`to_batches`(self, **kwargs)	Read the dataset as materialized record batches.
`to_table`(self, **kwargs)	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

count_rows(self, **kwargs)#

Count rows matching the scanner filter.

Parameters

**kwargsdict, optional: See scanner() method for full parameter description.

Returns

countint

get_fragments(self, Expression filter=None)#

Returns an iterator over the fragments in this dataset.

Parameters

filterExpression, default None: Return fragments matching the optional filter, either using the partition_expression or internal information like Parquet’s statistics.

Returns

fragmentsiterator of Fragment

head(self, int num_rows, **kwargs)#

Load the first N rows of the dataset.

Parameters

num_rowsint: The number of rows to load.
**kwargsdict, optional: See scanner() method for full parameter description.

Returns

tableTable

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_datasetdataset: The dataset to join to the current one, acting as the right dataset in the join operation.
keysstr or list[str]: The columns from current dataset that should be used as keys of the join operation left side.
right_keysstr 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_typestr, 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_suffixstr, 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_suffixstr, default None: Which suffic to add to the left column names. This prevents confusion when the columns in left and right datasets have colliding names.
coalesce_keysbool, default True: If the duplicated keys should be omitted from one of the sides in the join result.
use_threadsbool, default True: Whenever to use multithreading or not.

Returns

InMemoryDataset

partition_expression#: An Expression which evaluates to true for all data viewed by this 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

schemaSchema: The new dataset schema.

scanner(self, **kwargs)#

Build a scan operation against the dataset.

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

See the Scanner.from_dataset method for further information.

Parameters

**kwargsdict, optional: Arguments for Scanner.from_dataset.

Returns

scannerScanner

Examples

>>> import pyarrow.dataset as ds
>>> dataset = ds.dataset("path/to/dataset")

Selecting a subset of the columns:

>>> dataset.scanner(columns=["A", "B"]).to_table()

Projecting selected columns using an expression:

>>> dataset.scanner(columns={
...     "A_int": ds.field("A").cast("int64"),
... }).to_table()

Filtering rows while scanning:

>>> dataset.scanner(filter=ds.field("A") > 0).to_table()

schema#: The common schema of the full Dataset

take(self, indices, **kwargs)#

Select rows of data by index.

Parameters

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

Returns

tableTable

to_batches(self, **kwargs)#

Read the dataset as materialized record batches.

Parameters

**kwargsdict, optional: Arguments for Scanner.from_dataset.

Returns

record_batchesiterator of RecordBatch

to_table(self, **kwargs)#

Read the dataset to an Arrow table.

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

Parameters

**kwargsdict, optional: Arguments for Scanner.from_dataset.

Returns

tableTable

pyarrow.dataset.FilenamePartitioning

pyarrow.dataset.FileSystemDataset