GenericDataset

GenericDataset loads/saves data to a file using an underlying filesystem (e.g., local, S3, GCS).

kedro_datasets.pandas.GenericDataset

GenericDataset(
    *,
    filepath,
    file_format,
    load_args=None,
    save_args=None,
    version=None,
    credentials=None,
    fs_args=None,
    metadata=None
)

Bases: AbstractVersionedDataset[DataFrame, DataFrame]

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

Examples:

Using the YAML API:

cars:
  type: pandas.GenericDataset
  file_format: csv
  filepath: s3://data/01_raw/company/cars.csv
  load_args:
    sep: ","
    na_values: ["#NA", NA]
  save_args:
    index: False
    date_format: "%Y-%m-%d"

This second example is able to load a SAS7BDAT file via the pd.read_sas method. Trying to save this dataset will raise a DatasetError since pandas does not provide an equivalent pd.DataFrame.to_sas write method.

flights:
  type: pandas.GenericDataset
  file_format: sas
  filepath: data/01_raw/airplanes.sas7bdat
  load_args:
    format: sas7bdat

Using the Python API:

>>> import pandas as pd
>>> from kedro_datasets.pandas import GenericDataset
>>>
>>> data = pd.DataFrame({"col1": [1, 2], "col2": [4, 5], "col3": [5, 6]})
>>>
>>> dataset = GenericDataset(
...     filepath=tmp_path / "test.csv", file_format="csv", save_args={"index": False}
... )
>>> dataset.save(data)
>>> reloaded = dataset.load()
>>> assert data.equals(reloaded)

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 in the pandas.read_csv and pandas.DataFrame.to_csv will be identified. An error will be raised unless at least one matching read_{file_format} or to_{file_format} method is identified.

• load_args (dict[str, Any] | None, default: None ) –

  Pandas options for loading files. Here you can find all available arguments: https://pandas.pydata.org/pandas-docs/stable/reference/io.html All defaults are preserved.

• save_args (dict[str, Any] | None, default: None ) –

  Pandas options for saving files. Here you can find all available arguments: https://pandas.pydata.org/pandas-docs/stable/reference/io.html All defaults are preserved, but "index", which is set to False.

• version (Version | None, default: None ) –

  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 (dict[str, Any] | None, default: None ) –

  Credentials required to get access to the underlying filesystem. E.g. for GCSFileSystem it should look like {"token": None}.

• fs_args (dict[str, Any] | None, default: None ) –

  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 w when saving.

• metadata (dict[str, Any] | None, default: None ) –

  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.

Source code in kedro_datasets/pandas/generic_dataset.py

def __init__(  # noqa: PLR0913
    self,
    *,
    filepath: str,
    file_format: str,
    load_args: dict[str, Any] | None = None,
    save_args: dict[str, Any] | None = None,
    version: Version | None = None,
    credentials: dict[str, Any] | None = None,
    fs_args: dict[str, Any] | None = None,
    metadata: dict[str, Any] | None = None,
):
    """Creates a new instance of ``GenericDataset`` pointing to a concrete data file
    on a specific filesystem. The appropriate pandas load/save methods are
    dynamically identified by string matching on a best effort basis.

    Args:
        filepath: 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: String which is used to match the appropriate load/save method on a best
            effort basis. For example if 'csv' is passed in the `pandas.read_csv` and
            `pandas.DataFrame.to_csv` will be identified. An error will be raised unless
            at least one matching `read_{file_format}` or `to_{file_format}` method is
            identified.
        load_args: Pandas options for loading files.
            Here you can find all available arguments:
            https://pandas.pydata.org/pandas-docs/stable/reference/io.html
            All defaults are preserved.
        save_args: Pandas options for saving files.
            Here you can find all available arguments:
            https://pandas.pydata.org/pandas-docs/stable/reference/io.html
            All defaults are preserved, but "index", which is set to False.
        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: 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``), 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 `w` when saving.
        metadata: 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.
    """

    self._file_format = file_format.lower()

    _fs_args = deepcopy(fs_args) or {}
    _fs_open_args_load = _fs_args.pop("open_args_load", {})
    _fs_open_args_save = _fs_args.pop("open_args_save", {})
    _credentials = deepcopy(credentials) or {}

    protocol, path = get_protocol_and_path(filepath)
    if protocol == "file":
        _fs_args.setdefault("auto_mkdir", True)

    self._protocol = protocol
    self._fs = fsspec.filesystem(self._protocol, **_credentials, **_fs_args)

    self.metadata = metadata

    super().__init__(
        filepath=PurePosixPath(path),
        version=version,
        exists_function=self._fs.exists,
        glob_function=self._fs.glob,
    )

    # Handle default load and save and fs arguments
    self._load_args = {**self.DEFAULT_LOAD_ARGS, **(load_args or {})}
    self._save_args = {**self.DEFAULT_SAVE_ARGS, **(save_args or {})}
    self._fs_open_args_load = {
        **self.DEFAULT_FS_ARGS.get("open_args_load", {}),
        **(_fs_open_args_load or {}),
    }
    self._fs_open_args_save = {
        **self.DEFAULT_FS_ARGS.get("open_args_save", {}),
        **(_fs_open_args_save or {}),
    }

DEFAULT_FS_ARGS class-attribute instance-attribute

DEFAULT_FS_ARGS = {'open_args_save': {'mode': 'w'}}

DEFAULT_LOAD_ARGS class-attribute instance-attribute

DEFAULT_LOAD_ARGS = {}

DEFAULT_SAVE_ARGS class-attribute instance-attribute

DEFAULT_SAVE_ARGS = {}

_file_format instance-attribute

_file_format = lower()

_fs instance-attribute

_fs = filesystem(_protocol, **_credentials, **_fs_args)

_fs_open_args_load instance-attribute

_fs_open_args_load = {
    None: get("open_args_load", {}),
    None: _fs_open_args_load or {},
}

_fs_open_args_save instance-attribute

_fs_open_args_save = {
    None: get("open_args_save", {}),
    None: _fs_open_args_save or {},
}

_load_args instance-attribute

_load_args = {
    None: DEFAULT_LOAD_ARGS,
    None: load_args or {},
}

_protocol instance-attribute

_protocol = protocol

_save_args instance-attribute

_save_args = {
    None: DEFAULT_SAVE_ARGS,
    None: save_args or {},
}

metadata instance-attribute

metadata = metadata

_describe

_describe()

Source code in kedro_datasets/pandas/generic_dataset.py

def _describe(self) -> dict[str, Any]:
    return {
        "file_format": self._file_format,
        "filepath": self._filepath,
        "protocol": self._protocol,
        "load_args": self._load_args,
        "save_args": self._save_args,
        "version": self._version,
    }

_ensure_file_system_target

_ensure_file_system_target()

Source code in kedro_datasets/pandas/generic_dataset.py

def _ensure_file_system_target(self) -> None:
    # Fail fast if provided a known non-filesystem target
    if self._file_format in NON_FILE_SYSTEM_TARGETS:
        raise DatasetError(
            f"Cannot create a dataset of file_format '{self._file_format}' as it "
            f"does not support a filepath target/source."
        )

_exists

_exists()

Source code in kedro_datasets/pandas/generic_dataset.py

def _exists(self) -> bool:
    try:
        load_path = get_filepath_str(self._get_load_path(), self._protocol)
    except DatasetError:
        return False

    return self._fs.exists(load_path)

_invalidate_cache

_invalidate_cache()

Invalidate underlying filesystem caches.

Source code in kedro_datasets/pandas/generic_dataset.py

def _invalidate_cache(self) -> None:
    """Invalidate underlying filesystem caches."""
    filepath = get_filepath_str(self._filepath, self._protocol)
    self._fs.invalidate_cache(filepath)

_release

_release()

Source code in kedro_datasets/pandas/generic_dataset.py

def _release(self) -> None:
    super()._release()
    self._invalidate_cache()

load

load()

Source code in kedro_datasets/pandas/generic_dataset.py

def load(self) -> pd.DataFrame:
    self._ensure_file_system_target()

    load_path = get_filepath_str(self._get_load_path(), self._protocol)
    load_method = getattr(pd, f"read_{self._file_format}", None)
    if load_method:
        with self._fs.open(load_path, **self._fs_open_args_load) as fs_file:
            return load_method(fs_file, **self._load_args)
    raise DatasetError(
        f"Unable to retrieve 'pandas.read_{self._file_format}' method, please ensure that your "
        "'file_format' parameter has been defined correctly as per the Pandas API "
        "https://pandas.pydata.org/docs/reference/io.html"
    )

save

save(data)

Source code in kedro_datasets/pandas/generic_dataset.py

def save(self, data: pd.DataFrame) -> None:
    self._ensure_file_system_target()

    save_path = get_filepath_str(self._get_save_path(), self._protocol)
    save_method = getattr(data, f"to_{self._file_format}", None)
    if save_method:
        with self._fs.open(save_path, **self._fs_open_args_save) as fs_file:
            # KEY ASSUMPTION - first argument is path/buffer/io
            save_method(fs_file, **self._save_args)
            self._invalidate_cache()
    else:
        raise DatasetError(
            f"Unable to retrieve 'pandas.DataFrame.to_{self._file_format}' method, please "
            "ensure that your 'file_format' parameter has been defined correctly as "
            "per the Pandas API "
            "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html"
        )