kedro.extras.datasets.pandas.ParquetDataSet

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

Bases: kedro.io.core.AbstractVersionedDataSet

ParquetDataSet loads/saves data from/to a Parquet file using an underlying filesystem (e.g.: local, S3, GCS). It uses pandas to handle the Parquet file.

Example:

from kedro.extras.datasets.pandas import ParquetDataSet
import pandas as pd

data = pd.DataFrame({'col1': [1, 2], 'col2': [4, 5],
                     'col3': [5, 6]})

# data_set = ParquetDataSet(filepath="gcs://bucket/test.parquet")
data_set = ParquetDataSet(filepath="test.parquet")
data_set.save(data)
reloaded = data_set.load()
assert data.equals(reloaded)

Attributes

ParquetDataSet.DEFAULT_LOAD_ARGS
ParquetDataSet.DEFAULT_SAVE_ARGS

Methods

ParquetDataSet.__init__(filepath[, …]) Creates a new instance of ParquetDataSet pointing to a concrete Parquet file on a specific filesystem.
ParquetDataSet.exists() Checks whether a data set’s output already exists by calling the provided _exists() method.
ParquetDataSet.from_config(name, config[, …]) Create a data set instance using the configuration provided.
ParquetDataSet.get_last_load_version() Versioned datasets should override this property to return last loaded version
ParquetDataSet.get_last_save_version() Versioned datasets should override this property to return last saved version.
ParquetDataSet.invalidate_cache() Invalidate underlying filesystem caches.
ParquetDataSet.load() Loads data by delegation to the provided load method.
ParquetDataSet.release() Release any cached data.
ParquetDataSet.save(data) Saves data by delegation to the provided save method.
DEFAULT_LOAD_ARGS = {}
DEFAULT_SAVE_ARGS = {}
__init__(filepath, load_args=None, save_args=None, version=None, credentials=None, fs_args=None, layer=None)[source]

Creates a new instance of ParquetDataSet pointing to a concrete Parquet file on a specific filesystem.

Parameters:
Return type:

None

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 (str) – Data set name.
  • config (Dict[str, Any]) – Data set config dictionary.
  • load_version (Optional[str]) – 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 (Optional[str]) – 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.
Return type:

AbstractDataSet

Returns:

An instance of an AbstractDataSet subclass.

Raises:

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

get_last_load_version()

Versioned datasets should override this property to return last loaded version

Return type:Optional[str]
get_last_save_version()

Versioned datasets should override this property to return last saved version.

Return type:Optional[str]
invalidate_cache()[source]

Invalidate underlying filesystem caches.

Return type:None
load()

Loads data by delegation to the provided load method.

Return type:Any
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 exists method raises error.
Return type:None
save(data)

Saves data by delegation to the provided save method.

Parameters:data (Any) – the value to be saved by provided save method.
Raises:DataSetError – when underlying save method raises error.
Return type:None