Source code for kedro.extras.datasets.pandas.feather_dataset

"""``FeatherDataSet`` is a data set used to load and save data to feather files
using an underlying filesystem (e.g.: local, S3, GCS). The underlying functionality
is supported by pandas, so it supports all operations the pandas supports.
import logging
from copy import deepcopy
from io import BytesIO
from pathlib import PurePosixPath
from typing import Any, Dict

import fsspec
import pandas as pd

from import (

logger = logging.getLogger(__name__)

# NOTE: kedro.extras.datasets will be removed in Kedro 0.19.0.
# Any contribution to datasets should be made in kedro-datasets
# in kedro-plugins (

[docs]class FeatherDataSet(AbstractVersionedDataSet[pd.DataFrame, pd.DataFrame]): """``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 <\ data_catalog.html#use-the-data-catalog-with-the-yaml-api>`_: .. code-block:: yaml 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 <\ data_catalog.html#use-the-data-catalog-with-the-code-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") >>> >>> >>> reloaded = data_set.load() >>> >>> assert data.equals(reloaded) """ DEFAULT_LOAD_ARGS = {} # type: Dict[str, Any] DEFAULT_SAVE_ARGS = {} # type: Dict[str, Any] # pylint: disable=too-many-arguments
[docs] def __init__( self, filepath: str, load_args: Dict[str, Any] = None, save_args: Dict[str, Any] = None, version: Version = None, credentials: Dict[str, Any] = None, fs_args: Dict[str, Any] = None, ) -> None: """Creates a new instance of ``FeatherDataSet`` pointing to a concrete filepath. Args: filepath: 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: Pandas options for loading feather files. Here you can find all available arguments: All defaults are preserved. save_args: Pandas options for saving feather files. Here you can find all available arguments: All defaults are preserved. version: If specified, should be an instance of ````. If its ``load`` attribute is None, the latest version will be loaded. If its ``save`` attribute is None, save version will be autogenerated. credentials: Credentials required to get access to the underlying filesystem. E.g. for ``GCSFileSystem`` it should look like `{"token": None}`. fs_args: Extra arguments to pass into underlying filesystem class constructor (e.g. `{"project": "my-project"}` for ``GCSFileSystem``). """ _fs_args = deepcopy(fs_args) or {} _credentials = deepcopy(credentials) or {} protocol, path = get_protocol_and_path(filepath, version) if protocol == "file": _fs_args.setdefault("auto_mkdir", True) self._protocol = protocol self._storage_options = {**_credentials, **_fs_args} self._fs = fsspec.filesystem(self._protocol, **self._storage_options) super().__init__( filepath=PurePosixPath(path), version=version, exists_function=self._fs.exists, glob_function=self._fs.glob, ) # Handle default load argument self._load_args = deepcopy(self.DEFAULT_LOAD_ARGS) if load_args is not None: self._load_args.update(load_args) self._save_args = deepcopy(self.DEFAULT_SAVE_ARGS) if save_args is not None: self._save_args.update(save_args) if "storage_options" in self._save_args or "storage_options" in self._load_args: logger.warning( "Dropping 'storage_options' for %s, " "please specify them under 'fs_args' or 'credentials'.", self._filepath, ) self._save_args.pop("storage_options", None) self._load_args.pop("storage_options", None)
def _describe(self) -> Dict[str, Any]: return dict( filepath=self._filepath, protocol=self._protocol, load_args=self._load_args, version=self._version, ) def _load(self) -> pd.DataFrame: load_path = str(self._get_load_path()) if self._protocol == "file": # file:// protocol seems to misbehave on Windows # (<urlopen error file not on local host>), # so we don't join that back to the filepath; # storage_options also don't work with local paths return pd.read_feather(load_path, **self._load_args) load_path = f"{self._protocol}{PROTOCOL_DELIMITER}{load_path}" return pd.read_feather( load_path, storage_options=self._storage_options, **self._load_args ) def _save(self, data: pd.DataFrame) -> None: save_path = get_filepath_str(self._get_save_path(), self._protocol) buf = BytesIO() data.to_feather(buf, **self._save_args) with, mode="wb") as fs_file: fs_file.write(buf.getvalue()) self._invalidate_cache() def _exists(self) -> bool: load_path = get_filepath_str(self._get_load_path(), self._protocol) return self._fs.exists(load_path) def _release(self) -> None: super()._release() self._invalidate_cache() def _invalidate_cache(self) -> None: """Invalidate underlying filesystem caches.""" filepath = get_filepath_str(self._filepath, self._protocol) self._fs.invalidate_cache(filepath)