kedro.extras.datasets.pandas.FeatherDataSet

class kedro.extras.datasets.pandas.FeatherDataSet(filepath, load_args=None, version=None, credentials=None, fs_args=None)[source]

Bases: kedro.io.core.AbstractVersionedDataSet

FeatherDataSet loads and saves data to a feather file using an underlying filesystem (e.g.: local, S3, GCS). The underlying functionality is supported by pandas, so it supports all allowed pandas options for loading and saving csv files.

Example:

from kedro.extras.datasets.pandas import FeatherDataSet
import pandas as pd

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

# data_set = FeatherDataSet(filepath="gcs://bucket/test.feather")
data_set = FeatherDataSet(filepath="test.feather")

data_set.save(data)
reloaded = data_set.load()

assert data.equals(reloaded)

Attributes

FeatherDataSet.DEFAULT_LOAD_ARGS

Methods

FeatherDataSet.__init__(filepath[, …]) Creates a new instance of FeatherDataSet pointing to a concrete filepath.
FeatherDataSet.exists() Checks whether a data set’s output already exists by calling the provided _exists() method.
FeatherDataSet.from_config(name, config[, …]) Create a data set instance using the configuration provided.
FeatherDataSet.load() Loads data by delegation to the provided load method.
FeatherDataSet.release() Release any cached data.
FeatherDataSet.resolve_load_version() Compute the version the dataset should be loaded with.
FeatherDataSet.resolve_save_version() Compute the version the dataset should be saved with.
FeatherDataSet.save(data) Saves data by delegation to the provided save method.
DEFAULT_LOAD_ARGS = {}
__init__(filepath, load_args=None, version=None, credentials=None, fs_args=None)[source]

Creates a new instance of FeatherDataSet pointing to a concrete filepath.

Parameters:
  • filepath (str) – Filepath to a feather 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. Note: http(s) doesn’t support versioning.
  • load_args (Optional[Dict[str, Any]]) – Pandas options for loading feather files. Here you can find all available arguments: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_feather.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), as well as to pass to the filesystem’s open method through nested keys open_args_load and open_args_save. Here you can find all available arguments for open: https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.open All defaults are preserved, except mode, which is set to wb when saving.
Return type:

None

exists()

Checks whether a data set’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)

Create a data set 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 data set is versioned. Has no effect on the data set if versioning was not enabled.
  • save_version (Optional[str]) – Version string to be used for save operation if the data set is versioned. Has no effect on the data set if versioning was not enabled.
Return type:

AbstractDataSet

Returns:

An instance of an AbstractDataSet subclass.

Raises:

DataSetError – When the function fails to create the data set from its config.

load()

Loads data by delegation to the provided load method.

Return type:Any
Returns:Data returned by the provided load method.
Raises:DataSetError – When underlying load method raises error.
release()

Release any cached data.

Raises:DataSetError – when underlying release method raises error.
Return type:None
resolve_load_version()

Compute the version the dataset should be loaded with.

Return type:Optional[str]
resolve_save_version()

Compute the version the dataset should be saved with.

Return type:Optional[str]
save(data)

Saves data by delegation to the provided save method.

Parameters:data (Any) – the value to be saved by provided save method.
Raises:DataSetError – when underlying save method raises error.
Return type:None