"""``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
import fsspec
import pandas as pd
from kedro.io.core import (
AbstractVersionedDataset,
DatasetError,
Version,
get_filepath_str,
get_protocol_and_path,
)
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 usage for the
`YAML API <https://kedro.readthedocs.io/en/stable/data/\
data_catalog_yaml_examples.html>`_:
.. 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 ``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.
.. code-block:: yaml
flights:
type: pandas.GenericDataset
file_format: sas
filepath: data/01_raw/airplanes.sas7bdat
load_args:
format: sas7bdat
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 GenericDataset
>>> import pandas as pd
>>>
>>> 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)
"""
DEFAULT_LOAD_ARGS: dict[str, Any] = {}
DEFAULT_SAVE_ARGS: dict[str, Any] = {}
[docs]
def __init__( # noqa: PLR0913
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,
metadata: 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.
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,
)
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 {
"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)
