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

"""``CSVDataSet`` loads/saves data from/to a CSV file using an underlying
filesystem (e.g.: local, S3, GCS). It uses pandas to handle the CSV file.
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 CSVDataSet(AbstractVersionedDataset[pd.DataFrame, pd.DataFrame]): """``CSVDataSet`` loads/saves data from/to a CSV file using an underlying filesystem (e.g.: local, S3, GCS). It uses pandas to handle the CSV file. Example usage for the `YAML API <\ data_catalog_yaml_examples.html>`_: .. code-block:: yaml cars: type: pandas.CSVDataSet filepath: data/01_raw/company/cars.csv load_args: sep: "," na_values: ["#NA", NA] save_args: index: False date_format: "%Y-%m-%d %H:%M" decimal: . motorbikes: type: pandas.CSVDataSet filepath: s3://your_bucket/data/02_intermediate/company/motorbikes.csv credentials: dev_s3 Example usage for the `Python API <\ advanced_data_catalog_usage.html>`_: :: >>> from kedro.extras.datasets.pandas import CSVDataSet >>> import pandas as pd >>> >>> data = pd.DataFrame({'col1': [1, 2], 'col2': [4, 5], >>> 'col3': [5, 6]}) >>> >>> data_set = CSVDataSet(filepath="test.csv") >>> >>> reloaded = data_set.load() >>> assert data.equals(reloaded) """ DEFAULT_LOAD_ARGS = {} # type: Dict[str, Any] DEFAULT_SAVE_ARGS = {"index": False} # type: Dict[str, Any]
[docs] def __init__( # noqa: too-many-arguments 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 ``CSVDataSet`` pointing to a concrete CSV file on a specific filesystem. Args: filepath: Filepath in POSIX format to a CSV 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 CSV files. Here you can find all available arguments: All defaults are preserved. save_args: Pandas options for saving CSV files. Here you can find all available arguments: All defaults are preserved, but "index", which is set to False. 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 and save arguments 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 { "filepath": self._filepath, "protocol": self._load_args, "save_args": self._save_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_csv(load_path, **self._load_args) load_path = f"{self._protocol}{PROTOCOL_DELIMITER}{load_path}" return pd.read_csv( 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_csv(path_or_buf=buf, **self._save_args) with, mode="wb") as fs_file: fs_file.write(buf.getvalue()) self._invalidate_cache() 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 _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)