csv_dataset.py

"""``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
import warnings
from copy import deepcopy
from io import BytesIO
from pathlib import PurePosixPath
from typing import Any, Dict

import fsspec
import pandas as pd
from kedro.io.core import (
    PROTOCOL_DELIMITER,
    Version,
    get_filepath_str,
    get_protocol_and_path,
)

from kedro_datasets import KedroDeprecationWarning
from kedro_datasets._io import AbstractVersionedDataset, DatasetError

logger = logging.getLogger(__name__)


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 <https://kedro.readthedocs.io/en/stable/data/\
    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 <https://kedro.readthedocs.io/en/stable/data/\
    advanced_data_catalog_usage.html>`_:

    .. code-block:: pycon

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

    """

    DEFAULT_LOAD_ARGS: Dict[str, Any] = {}
    DEFAULT_SAVE_ARGS: Dict[str, Any] = {"index": False}

    def __init__(  # noqa: PLR0913
        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,
        metadata: 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:
                https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html
                All defaults are preserved.
            save_args: Pandas options for saving CSV files.
                Here you can find all available arguments:
                https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_csv.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``).
            metadata: Any Any arbitrary metadata.
                This is ignored by Kedro, but may be consumed by users or external plugins.
        """
        _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)

        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 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._protocol,
            "load_args": 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 self._fs.open(save_path, 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)

    def _preview(self, nrows: int = 40) -> Dict:
        # Create a copy so it doesn't contaminate the original dataset
        dataset_copy = self._copy()
        dataset_copy._load_args["nrows"] = nrows
        data = dataset_copy.load()

        return data.to_dict(orient="split")


_DEPRECATED_CLASSES = {
    "CSVDataSet": CSVDataset,
}


def __getattr__(name):
    if name in _DEPRECATED_CLASSES:
        alias = _DEPRECATED_CLASSES[name]
        warnings.warn(
            f"{repr(name)} has been renamed to {repr(alias.__name__)}, "
            f"and the alias will be removed in Kedro-Datasets 2.0.0",
            KedroDeprecationWarning,
            stacklevel=2,
        )
        return alias
    raise AttributeError(f"module {repr(__name__)} has no attribute {repr(name)}")