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)[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.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.load`()	Loads data by delegation to the provided load method.
`ParquetDataSet.release`()	Release any cached data.
`ParquetDataSet.resolve_load_version`()	Compute the version the dataset should be loaded with.
`ParquetDataSet.resolve_save_version`()	Compute the version the dataset should be saved with.
`ParquetDataSet.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, load_args=None, save_args=None, version=None, credentials=None, fs_args=None)[source]¶

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

Parameters

filepath (str) – Filepath in POSIX format to a Parquet 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. It can also be a path to a directory. If the directory is provided then it can be used for reading partitioned parquet files. Note: http(s) doesn’t support versioning.
load_args (Optional[Dict[str, Any]]) – Additional options for loading Parquet file(s). Here you can find all available arguments when reading single file: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_parquet.html Here you can find all available arguments when reading partitioned datasets: https://arrow.apache.org/docs/python/generated/pyarrow.parquet.ParquetDataset.html#pyarrow.parquet.ParquetDataset.read All defaults are preserved.
save_args (Optional[Dict[str, Any]]) – Additional saving options for pyarrow.parquet.write_table and pyarrow.Table.from_pandas. Here you can find all available arguments for write_table(): https://arrow.apache.org/docs/python/generated/pyarrow.parquet.write_table.html?highlight=write_table#pyarrow.parquet.write_table The arguments for from_pandas() should be passed through a nested key: from_pandas. E.g.: save_args = {“from_pandas”: {“preserve_index”: False}} Here you can find all available arguments for from_pandas(): https://arrow.apache.org/docs/python/generated/pyarrow.Table.html#pyarrow.Table.from_pandas
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.

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.

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 release method raises error.
Return type: None

resolve_load_version()¶

Compute the version the dataset should be loaded with.

Return type: Optional[str]

resolve_save_version()¶

Compute the version the dataset should be saved with.

Return type: Optional[str]

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