kedro_datasets.polars.EagerPolarsDataset¶

class kedro_datasets.polars.EagerPolarsDataset(*, filepath, file_format, load_args=None, save_args=None, version=None, credentials=None, fs_args=None, metadata=None)[source]¶

polars.EagerPolarsDataset loads/saves data from/to a data file using an underlying filesystem (e.g.: local, S3, GCS). It uses polars to handle the dynamically select the appropriate type of read/write on a best effort basis.

Example usage for the YAML API:

cars:
  type: polars.EagerPolarsDataset
  file_format: parquet
  filepath: s3://data/01_raw/company/cars.parquet
  load_args:
    low_memory: True
  save_args:
    compression: "snappy"

Example using Python API:

from kedro_datasets.polars import EagerPolarsDataset
import polars as pl

data = pl.DataFrame({"col1": [1, 2], "col2": [4, 5], "col3": [5, 6]})

dataset = EagerPolarsDataset(filepath=tmp_path / "test.parquet", file_format="parquet")
dataset.save(data)
reloaded = dataset.load()
assert data.equals(reloaded)

Attributes

`DEFAULT_LOAD_ARGS`
`DEFAULT_SAVE_ARGS`

Methods

`exists`()	Checks whether a dataset's output already exists by calling the provided _exists() method.
`from_config`(name, config[, load_version, ...])	Create a dataset instance using the configuration provided.
`load`()	Loads data by delegation to the provided load method.
`release`()	Release any cached data.
`resolve_load_version`()	Compute the version the dataset should be loaded with.
`resolve_save_version`()	Compute the version the dataset should be saved with.
`save`(data)	Saves data by delegation to the provided save method.
`to_config`()	Converts the dataset instance into a dictionary-based configuration for serialization.

DEFAULT_LOAD_ARGS: dict[str, Any] = {}¶

DEFAULT_SAVE_ARGS: dict[str, Any] = {}¶

__init__(*, filepath, file_format, load_args=None, save_args=None, version=None, credentials=None, fs_args=None, metadata=None)[source]¶

Creates a new instance of EagerPolarsDataset pointing to a concrete data file on a specific filesystem. The appropriate polars load/save methods are dynamically identified by string matching on a best effort basis.

Parameters:

filepath (str) – Filepath in POSIX format to a file prefixed with a protocol like s3://. If prefix is not provided, file protocol (local filesystem) will be used. The prefix should be any protocol supported by fsspec. Key assumption: The first argument of either load/save method points to a filepath/buffer/io type location. There are some read/write targets such as ‘clipboard’ or ‘records’ that will fail since they do not take a filepath like argument.
file_format (str) – String which is used to match the appropriate load/save method on a best effort basis. For example if ‘csv’ is passed, the polars.read_csv and polars.DataFrame.write_csv methods will be identified. An error will be raised unless there is at least one matching read_<file_format> or write_<file_format>.
load_args (Optional[dict[str, Any]]) – Polars options for loading CSV files. Here you can find all available arguments: https://pola-rs.github.io/polars/py-polars/html/reference/io.html All defaults are preserved.
save_args (Optional[dict[str, Any]]) – Polars options for saving files. Here you can find all available arguments: https://pola-rs.github.io/polars/py-polars/html/reference/io.html All defaults are preserved.
version (Optional[Version]) – If specified, should be an instance of kedro.io.core.Version. If its load attribute is None, the latest version will be loaded. If its save attribute is None, save version will be autogenerated.
credentials (Optional[dict[str, Any]]) – Credentials required to get access to the underlying filesystem. E.g. for GCSFileSystem it should look like {“token”: None}.
fs_args (Optional[dict[str, Any]]) – Extra arguments to pass into underlying filesystem class constructor (e.g. {“project”: “my-project”} for GCSFileSystem).
metadata (Optional[dict[str, Any]]) – Any arbitrary metadata. This is ignored by Kedro, but may be consumed by users or external plugins.

Raises:

DatasetError – Will be raised if at least less than one appropriate read or write methods are identified.

exists()[source]¶

Checks whether a dataset’s output already exists by calling the provided _exists() method.

Return type:: bool
Returns:: Flag indicating whether the output already exists.
Raises:: DatasetError – when underlying exists method raises error.

classmethod from_config(name, config, load_version=None, save_version=None)[source]¶

Create a dataset instance using the configuration provided.

Parameters:

name (str) – Data set name.
config (dict[str, Any]) – Data set config dictionary.
load_version (Optional[str]) – Version string to be used for load operation if the dataset is versioned. Has no effect on the dataset if versioning was not enabled.
save_version (Optional[str]) – Version string to be used for save operation if the dataset is versioned. Has no effect on the dataset if versioning was not enabled.

Return type:

AbstractDataset

Returns:

An instance of an AbstractDataset subclass.

Raises:

DatasetError – When the function fails to create the dataset from its config.

load()[source]¶

Loads data by delegation to the provided load method.

Return type:: DataFrame
Returns:: Data returned by the provided load method.
Raises:: DatasetError – When underlying load method raises error.

release()[source]¶

Release any cached data.

Raises:: DatasetError – when underlying release method raises error.
Return type:: None

resolve_load_version()[source]¶

Compute the version the dataset should be loaded with.

Return type:: Optional[str]

resolve_save_version()[source]¶

Compute the version the dataset should be saved with.

Return type:: Optional[str]

save(data)[source]¶

Saves data by delegation to the provided save method.

Parameters:

data (DataFrame) – the value to be saved by provided save method.

Raises:

DatasetError – when underlying save method raises error.
FileNotFoundError – when save method got file instead of dir, on Windows.
NotADirectoryError – when save method got file instead of dir, on Unix.

Return type:

None

to_config()[source]¶

Converts the dataset instance into a dictionary-based configuration for serialization. Ensures that any subclass-specific details are handled, with additional logic for versioning and caching implemented for CachedDataset.

Adds a key for the dataset’s type using its module and class name and includes the initialization arguments.

For CachedDataset it extracts the underlying dataset’s configuration, handles the versioned flag and removes unnecessary metadata. It also ensures the embedded dataset’s configuration is appropriately flattened or transformed.

If the dataset has a version key, it sets the versioned flag in the configuration.

Removes the metadata key from the configuration if present.

Return type:: dict[str, Any]
Returns:: A dictionary containing the dataset’s type and initialization arguments.