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

"""``GenericDataSet`` loads/saves data from/to a data file using an underlying
filesystem (e.g.: local, S3, GCS). It uses pandas to handle the
type of read/write target.
"""
from copy import deepcopy
from pathlib import PurePosixPath
from typing import Any, Dict

import fsspec
import pandas as pd

from kedro.io.core import (
    AbstractVersionedDataSet,
    DataSetError,
    Version,
    get_filepath_str,
    get_protocol_and_path,
)

# 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 (https://github.com/kedro-org/kedro-plugins)


NON_FILE_SYSTEM_TARGETS = [
    "clipboard",
    "numpy",
    "sql",
    "period",
    "records",
    "timestamp",
    "xarray",
    "sql_table",
]


[docs]class GenericDataSet(AbstractVersionedDataSet[pd.DataFrame, pd.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. Example using `YAML API <https://kedro.readthedocs.io/en/stable/data/\ data_catalog.html#use-the-data-catalog-with-the-yaml-api>`_: .. code-block:: yaml >>> 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 :code:`pd.read_sas` method. Trying to save this dataset will raise a `DataSetError` since pandas does not provide an equivalent :code:`pd.DataFrame.to_sas` write method. .. code-block:: yaml >>> flights: >>> type: pandas.GenericDataSet >>> file_format: sas >>> filepath: data/01_raw/airplanes.sas7bdat >>> load_args: >>> format: sas7bdat Example using Python API: :: >>> from kedro.extras.datasets.pandas import GenericDataSet >>> import pandas as pd >>> >>> data = pd.DataFrame({'col1': [1, 2], 'col2': [4, 5], >>> 'col3': [5, 6]}) >>> >>> data_set = GenericDataSet(filepath="test.csv", file_format='csv') >>> data_set.save(data) >>> 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, file_format: 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, ): """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 `r` when loading and to `w` when saving. 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) super().__init__( filepath=PurePosixPath(path), version=version, exists_function=self._fs.exists, glob_function=self._fs.glob, ) 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) _fs_open_args_save.setdefault("mode", "w") self._fs_open_args_load = _fs_open_args_load self._fs_open_args_save = _fs_open_args_save
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." ) 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" ) 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" ) 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) def _describe(self) -> Dict[str, Any]: return dict( file_format=self._file_format, filepath=self._filepath, protocol=self._protocol, load_args=self._load_args, save_args=self._save_args, version=self._version, ) 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)