ray.data.read_csv#
- ray.data.read_csv(paths: str | List[str], *, filesystem: pyarrow.fs.FileSystem | None = None, parallelism: int = -1, ray_remote_args: Dict[str, Any] = None, arrow_open_stream_args: Dict[str, Any] | None = None, meta_provider: BaseFileMetadataProvider | None = None, partition_filter: PathPartitionFilter | None = None, partitioning: Partitioning = Partitioning(style='hive', base_dir='', field_names=None, filesystem=None), include_paths: bool = False, ignore_missing_paths: bool = False, shuffle: Literal['files'] | None = None, file_extensions: List[str] | None = None, **arrow_csv_args) Dataset [source]#
Creates a
Dataset
from CSV files.Examples
Read a file in remote storage.
>>> import ray >>> ds = ray.data.read_csv("s3://anonymous@ray-example-data/iris.csv") >>> ds.schema() Column Type ------ ---- sepal length (cm) double sepal width (cm) double petal length (cm) double petal width (cm) double target int64
Read multiple local files.
>>> ray.data.read_csv( ... ["local:///path/to/file1", "local:///path/to/file2"])
Read a directory from remote storage.
>>> ds = ray.data.read_csv("s3://anonymous@ray-example-data/iris-csv/")
Read files that use a different delimiter. For more uses of ParseOptions see https://arrow.apache.org/docs/python/generated/pyarrow.csv.ParseOptions.html # noqa: #501
>>> from pyarrow import csv >>> parse_options = csv.ParseOptions(delimiter="\t") >>> ds = ray.data.read_csv( ... "s3://anonymous@ray-example-data/iris.tsv", ... parse_options=parse_options) >>> ds.schema() Column Type ------ ---- sepal.length double sepal.width double petal.length double petal.width double variety string
Convert a date column with a custom format from a CSV file. For more uses of ConvertOptions see https://arrow.apache.org/docs/python/generated/pyarrow.csv.ConvertOptions.html # noqa: #501
>>> from pyarrow import csv >>> convert_options = csv.ConvertOptions( ... timestamp_parsers=["%m/%d/%Y"]) >>> ds = ray.data.read_csv( ... "s3://anonymous@ray-example-data/dow_jones.csv", ... convert_options=convert_options)
By default,
read_csv()
parses Hive-style partitions from file paths. If your data adheres to a different partitioning scheme, set thepartitioning
parameter.>>> ds = ray.data.read_csv("s3://anonymous@ray-example-data/year=2022/month=09/sales.csv") >>> ds.take(1) [{'order_number': 10107, 'quantity': 30, 'year': '2022', 'month': '09'}]
By default,
read_csv()
reads all files from file paths. If you want to filter files by file extensions, set thepartition_filter
parameter.Read only
*.csv
files from a directory.>>> ray.data.read_csv("s3://anonymous@ray-example-data/different-extensions/", ... file_extensions=["csv"]) Dataset(num_blocks=..., num_rows=1, schema={a: int64, b: int64})
- Parameters:
paths – A single file or directory, or a list of file or directory paths. A list of paths can contain both files and directories.
filesystem – The PyArrow filesystem implementation to read from. These filesystems are specified in the pyarrow docs. Specify this parameter if you need to provide specific configurations to the filesystem. By default, the filesystem is automatically selected based on the scheme of the paths. For example, if the path begins with
s3://
, theS3FileSystem
is used.parallelism – The amount of parallelism to use for the dataset. Defaults to -1, which automatically determines the optimal parallelism for your configuration. You should not need to manually set this value in most cases. For details on how the parallelism is automatically determined and guidance on how to tune it, see Tuning read parallelism. Parallelism is upper bounded by the total number of records in all the CSV files.
ray_remote_args – kwargs passed to
remote()
in the read tasks.arrow_open_stream_args – kwargs passed to pyarrow.fs.FileSystem.open_input_file. when opening input files to read.
meta_provider – A file metadata provider. Custom metadata providers may be able to resolve file metadata more quickly and/or accurately. In most cases, you do not need to set this. If
None
, this function uses a system-chosen implementation.partition_filter – A
PathPartitionFilter
. Use with a custom callback to read only selected partitions of a dataset. By default, no files are filtered.partitioning –
A
Partitioning
object that describes how paths are organized. By default, this function parses Hive-style partitions.include_paths – If
True
, include the path to each file. File paths are stored in the'path'
column.ignore_missing_paths – If True, ignores any file paths in
paths
that are not found. Defaults to False.shuffle – If setting to “files”, randomly shuffle input files order before read. Defaults to not shuffle with
None
.arrow_csv_args – CSV read options to pass to pyarrow.csv.open_csv when opening CSV files.
file_extensions – A list of file extensions to filter files by.
- Returns:
Dataset
producing records read from the specified paths.