kedro.extras.datasets.pandas.GenericDataSet

class kedro.extras.datasets.pandas.GenericDataSet(filepath, file_format, load_args=None, save_args=None, version=None, credentials=None, fs_args=None)[source]

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:

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.

flights:
   type: pandas.GenericDataSet
   file_format: sas
   filepath: data/01_raw/airplanes.sas7bdat
   load_args:
      format: sas7bdat

Example usage for the 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="test.csv", file_format='csv')
data_set.save(data)
reloaded = data_set.load()
assert data.equals(reloaded)

Attributes

DEFAULT_LOAD_ARGS

DEFAULT_SAVE_ARGS

Methods

exists()

Checks whether a data set's output already exists by calling the provided _exists() method.

from_config(name, config[, load_version, ...])

Create a data set instance using the configuration provided.

load()

Loads data by delegation to the provided load method.

release()

Release any cached data.

resolve_load_version()

Compute the version the dataset should be loaded with.

resolve_save_version()

Compute the version the dataset should be saved with.

save(data)

Saves data by delegation to the provided save method.
DEFAULT_LOAD_ARGS: Dict[str, Any] = {}
DEFAULT_SAVE_ARGS: Dict[str, Any] = {}
__init__(filepath, file_format, load_args=None, save_args=None, version=None, credentials=None, fs_args=None)[source]

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.

Parameters:

  • filepath (str) – 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 (str) – 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 (Optional[Dict[str, Any]]) – 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 (Optional[Dict[str, Any]]) – 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 (Optional[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 (Optional[Dict[str, Any]]) – Credentials required to get access to the underlying filesystem. E.g. for GCSFileSystem it should look like {“token”: None}.

  • fs_args (Optional[Dict[str, Any]]) – 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.

exists()

Checks whether a data set’s output already exists by calling the provided _exists() method.

Return type:

bool

Returns:

Flag indicating whether the output already exists.

Raises:

DatasetError – when underlying exists method raises error.

classmethod from_config(name, config, load_version=None, save_version=None)

Create a data set instance using the configuration provided.

Parameters:

  • name – Data set name.

  • config – Data set config dictionary.

  • load_version – Version string to be used for load operation if the data set is versioned. Has no effect on the data set if versioning was not enabled.

  • save_version – Version string to be used for save operation if the data set is versioned. Has no effect on the data set if versioning was not enabled.

Returns:

An instance of an AbstractDataset subclass.

Raises:

DatasetError – When the function fails to create the data set from its config.

load()

Loads data by delegation to the provided load method.

Return type:

TypeVar(_DO)

Returns:

Data returned by the provided load method.

Raises:

DatasetError – When underlying load method raises error.

release()

Release any cached data.

Raises:

DatasetError – when underlying release method raises error.

Return type:

None

resolve_load_version()

Compute the version the dataset should be loaded with.

Return type:

str | None

resolve_save_version()

Compute the version the dataset should be saved with.

Return type:

str | None

save(data)

Saves data by delegation to the provided save method.

Parameters:

data (TypeVar(_DI)) – the value to be saved by provided save method.

Raises:

  • DatasetError – when underlying save method raises error.

  • FileNotFoundError – when save method got file instead of dir, on Windows.

  • NotADirectoryError – when save method got file instead of dir, on Unix.

Return type:

None