"""``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,
)
NON_FILE_SYSTEM_TARGETS = [
"clipboard",
"numpy",
"sql",
"period",
"records",
"timestamp",
"xarray",
"sql_table",
]
[docs]class GenericDataSet(AbstractVersionedDataSet):
"""`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/05_data/\
01_data_catalog.html#using-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="s3://test.csv", file_format='csv')
>>> 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) -> Any:
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)