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)

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 adding a catalog entry with YAML API:

boats:
  type: pandas.ParquetDataSet
  filepath: data/01_raw/boats.parquet
  load_args:
    engine: pyarrow
    use_nullable_dtypes: True
  save_args:
    file_scheme: hive
    has_nulls: False
    engine: pyarrow

trucks:
  type: pandas.ParquetDataSet
  filepath: abfs://container/02_intermediate/trucks.parquet
  credentials: dev_abs
  load_args:
    columns: [name, gear, disp, wt]
    index: name
  save_args:
    compression: GZIP
    partition_on: [name]

Example using Python API:

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

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, load_args=None, save_args=None, version=None, credentials=None, fs_args=None)

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.

• 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