kedro.extras.datasets.pandas.FeatherDataSet

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

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 usage for the YAML API:

cars:
  type: pandas.FeatherDataSet
  filepath: data/01_raw/company/cars.feather
  load_args:
    columns: ['col1', 'col2', 'col3']
    use_threads: True

motorbikes:
  type: pandas.FeatherDataSet
  filepath: s3://your_bucket/data/02_intermediate/company/motorbikes.feather
  credentials: dev_s3

Example usage for the Python API:

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="test.feather")

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

assert data.equals(reloaded)

Attributes

DEFAULT_LOAD_ARGS

DEFAULT_SAVE_ARGS

Methods

exists()

Checks whether a data set's output already exists by calling the provided _exists() method.

from_config(name, config[, load_version, ...])

Create a data set 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.
DEFAULT_LOAD_ARGS: Dict[str, Any] = {}
DEFAULT_SAVE_ARGS: Dict[str, Any] = {}
__init__(filepath, load_args=None, save_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 in POSIX format 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.

  • save_args (Optional[Dict[str, Any]]) – Pandas options for saving feather files. Here you can find all available arguments: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_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).

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 – Data set name.

  • config – Data set config dictionary.

  • load_version – 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 – 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.

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:

TypeVar(_DO)

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:

str | None

resolve_save_version()

Compute the version the dataset should be saved with.

Return type:

str | None

save(data)

Saves data by delegation to the provided save method.

Parameters:

data (TypeVar(_DI)) – 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