"""``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.
"""
from __future__ import annotations
import logging
from copy import deepcopy
from io import BytesIO
from pathlib import PurePosixPath
from typing import Any
import fsspec
import pandas as pd
from kedro.io.core import (
PROTOCOL_DELIMITER,
AbstractVersionedDataset,
DatasetError,
Version,
get_filepath_str,
get_protocol_and_path,
)
from kedro_datasets._typing import TablePreview
logger = logging.getLogger(__name__)
[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 <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=tmp_path / "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}
[docs]
def __init__( # noqa: PLR0913
self,
*,
filepath: 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,
) -> 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)
[docs]
def preview(self, nrows: int = 5) -> TablePreview:
"""
Generate a preview of the dataset with a specified number of rows.
Args:
nrows: The number of rows to include in the preview. Defaults to 5.
Returns:
dict: A dictionary containing the data in a split format.
"""
# Create a copy so it doesn't contaminate the original dataset
dataset_copy = self._copy()
dataset_copy._load_args["nrows"] = nrows # type: ignore[attr-defined]
data = dataset_copy.load()
return data.to_dict(orient="split")
