Kedro: [KED-922] IO Dataset class to load and save feather files

Created on 8 Jul 2019 · 2Comments · Source: quantumblacklabs/kedro

Description

A dataset class to load and save feather files.

Context

Feather is a useful format for large storage and quick access where it is cpp binding which gives a very good write and read speed. The feature format will be a good format for the intermediate storage files.

Possible Implementation

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"""FeatherLocalDataSet loads and saves data to a local feather format file
(https://github.com/wesm/feather). The
underlying functionality is supported by pandas, so it supports all operations the pandas supports.
"""
from pathlib import Path
from typing import Any, Dict

import pandas as pd

from kedro.io.core import AbstractDataSet, DataSetError, FilepathVersionMixIn, Version

class FeatherLocalDataSet(AbstractDataSet, FilepathVersionMixIn):
"""FeatherLocalDataSet loads and saves data to a local feather file. The
underlying functionality is supported by pandas, so it supports all
allowed pandas options for loading and saving csv files.

Example:
::

    >>> from kedro.contrib.io import FeatherLocalDataSet
    >>> import pandas as pd
    >>>
    >>> data = pd.DataFrame({'col1': [1, 2], 'col2': [4, 5],
    >>>                      'col3': [5, 6]})
    >>> data_set = FeatherLocalDataSet(filepath="test.feather",
    >>>                                  load_args=None,
    >>>                                  save_args={"index": False})
    >>> data_set.save(data)
    >>> reloaded = data_set.load()
    >>>
    >>> assert data.equals(reloaded)

"""

def _describe(self) -> Dict[str, Any]:
    return dict(
        filepath=self._filepath,
        load_args=self._load_args,
        save_args=self._save_args,
        version=self._version,
    )

def __init__(
        self,
        filepath: str,
        load_args: Dict[str, Any] = None,
        save_args: Dict[str, Any] = None,
        version: Version = None,
) -> None:
    """Creates a new instance of ``FeatherLocalDataSet`` pointing to a concrete
    filepath.

    Args:
        filepath: path to a feather file.
        load_args: feather 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#pandas.read_feather
            All defaults are preserved.
        save_args: None
        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.
    """
    default_save_args = {"index": False}
    default_load_args = {}
    self._filepath = filepath
    self._load_args = (
        {**default_load_args, **load_args}
        if load_args is not None
        else default_load_args
    )
    self._save_args = (
        {**default_save_args, **save_args}
        if save_args is not None
        else default_save_args
    )
    self._version = version

def _load(self) -> pd.DataFrame:
    load_path = self._get_load_path(self._filepath, self._version)
    return pd.read_feather(load_path, **self._load_args)

def _save(self, data: pd.DataFrame) -> None:
    save_path = Path(self._get_save_path(self._filepath, self._version))
    save_path.parent.mkdir(parents=True, exist_ok=True)
    data.to_feather(str(save_path))

    load_path = Path(self._get_load_path(self._filepath, self._version))
    self._check_paths_consistency(
        str(load_path.absolute()), str(save_path.absolute())
    )

def _exists(self) -> bool:
    try:
        path = self._get_load_path(self._filepath, self._version)
    except DataSetError:
        return False
    return Path(path).is_file()

Possible Alternatives

(Optional) Describe any alternative solutions or features you've considered.

Feature Request

Source

mdomarsaleem

Most helpful comment

Hi @mdomarsaleem, thank you for submitting this issue! Please feel free to create a pull request against this repository and add FeatherLocalDataSet to kedro.contrib.io. Please also add the corresponding unit tests for your dataset (example can be found in tests/contrib/io/azure/test_csv_blob.py).