Dataset API

Constructor

Dataset

A Dataset is a distributed data collection for data loading and processing.

Basic Transformations

Dataset.map	Apply the given function to each row of this dataset.
Dataset.map_batches	Apply the given function to batches of data.
Dataset.flat_map	Apply the given function to each row and then flatten results.
Dataset.filter	Filter out rows that don't satisfy the given predicate.
Dataset.add_column	Add the given column to the dataset.
Dataset.drop_columns	Drop one or more columns from the dataset.
Dataset.select_columns	Select one or more columns from the dataset.
Dataset.random_sample	Returns a new Dataset containing a random fraction of the rows.
Dataset.limit	Truncate the dataset to the first limit rows.

Sorting, Shuffling, Repartitioning

Dataset.sort	Sort the dataset by the specified key column or key function.
Dataset.random_shuffle	Randomly shuffle the rows of this Dataset.
Dataset.randomize_block_order	Randomly shuffle the blocks of this Dataset.
Dataset.repartition	Repartition the Dataset into exactly this number of blocks.

Splitting and Merging Datasets

Dataset.split	Materialize and split the dataset into n disjoint pieces.
Dataset.split_at_indices	Materialize and split the dataset at the given indices (like np.split).
Dataset.split_proportionately	Materialize and split the dataset using proportions.
Dataset.streaming_split	Returns n DataIterators that can be used to read disjoint subsets of the dataset in parallel.
Dataset.train_test_split	Materialize and split the dataset into train and test subsets.
Dataset.union	Concatenate Datasets across rows.
Dataset.zip	Materialize and zip the columns of this dataset with the columns of another.

Grouped and Global Aggregations

Dataset.groupby	Group rows of a Dataset according to a column.
Dataset.unique	List the unique elements in a given column.
Dataset.aggregate	Aggregate values using one or more functions.
Dataset.sum	Compute the sum of one or more columns.
Dataset.min	Return the minimum of one or more columns.
Dataset.max	Return the maximum of one or more columns.
Dataset.mean	Compute the mean of one or more columns.
Dataset.std	Compute the standard deviation of one or more columns.

Consuming Data

Dataset.show	Print up to the given number of rows from the Dataset.
Dataset.take	Return up to limit rows from the Dataset.
Dataset.take_batch	Return up to batch_size rows from the Dataset in a batch.
Dataset.take_all	Return all of the rows in this Dataset.
Dataset.iterator	Return a DataIterator over this dataset.
Dataset.iter_rows	Return an iterable over the rows in this dataset.
Dataset.iter_batches	Return an iterable over batches of data.
Dataset.iter_torch_batches	Return an iterable over batches of data represented as Torch tensors.
Dataset.iter_tf_batches	Return an iterable over batches of data represented as TensorFlow tensors.

I/O and Conversion

Dataset.write_parquet	Writes the Dataset to parquet files under the provided path.
Dataset.write_json	Writes the Dataset to JSON and JSONL files.
Dataset.write_csv	Writes the Dataset to CSV files.
Dataset.write_numpy	Writes a column of the Dataset to .npy files.
Dataset.write_tfrecords	Write the Dataset to TFRecord files.
Dataset.write_webdataset	Writes the dataset to WebDataset files.
Dataset.write_mongo	Writes the Dataset to a MongoDB database.
Dataset.write_datasource	Writes the dataset to a custom Datasource.
Dataset.to_torch	Return a Torch IterableDataset over this Dataset.
Dataset.to_tf	Return a TensorFlow Dataset over this Dataset.
Dataset.to_dask	Convert this Dataset into a Dask DataFrame.
Dataset.to_mars	Convert this Dataset into a Mars DataFrame.
Dataset.to_modin	Convert this Dataset into a Modin DataFrame.
Dataset.to_spark	Convert this Dataset into a Spark DataFrame.
Dataset.to_pandas	Convert this Dataset to a single pandas DataFrame.
Dataset.to_pandas_refs	Converts this Dataset into a distributed set of Pandas dataframes.
Dataset.to_numpy_refs	Converts this Dataset into a distributed set of NumPy ndarrays or dictionary of NumPy ndarrays.
Dataset.to_arrow_refs	Convert this Dataset into a distributed set of PyArrow tables.
Dataset.to_random_access_dataset	Convert this dataset into a distributed RandomAccessDataset (EXPERIMENTAL).

Inspecting Metadata

Dataset.count	Count the number of records in the dataset.
Dataset.columns	Returns the columns of this Dataset.
Dataset.schema	Return the schema of the dataset.
Dataset.num_blocks	Return the number of blocks of this dataset.
Dataset.size_bytes	Return the in-memory size of the dataset.
Dataset.input_files	Return the list of input files for the dataset.
Dataset.stats	Returns a string containing execution timing information.
Dataset.get_internal_block_refs	Get a list of references to the underlying blocks of this dataset.

Execution

Dataset.materialize	Execute and materialize this dataset into object store memory.
ActorPoolStrategy	Specify the compute strategy for a Dataset transform.

Serialization

Dataset.has_serializable_lineage	Whether this dataset's lineage is able to be serialized for storage and later deserialized, possibly on a different cluster.
Dataset.serialize_lineage	Serialize this dataset's lineage, not the actual data or the existing data futures, to bytes that can be stored and later deserialized, possibly on a different cluster.
Dataset.deserialize_lineage	Deserialize the provided lineage-serialized Dataset.

Internals

block.Block	alias of Union[pyarrow.Table, pandas.DataFrame]
block.BlockExecStats	Execution stats for this block.
block.BlockMetadata	Metadata about the block.
block.BlockAccessor	Provides accessor methods for a specific block.