kedro_datasets.pandas.HDFDataset

class kedro_datasets.pandas.HDFDataset(*, filepath, key, load_args=None, save_args=None, version=None, credentials=None, fs_args=None, metadata=None)[source]

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

Example usage for the YAML API:

hdf_dataset:
  type: pandas.HDFDataset
  filepath: s3://my_bucket/raw/sensor_reading.h5
  credentials: aws_s3_creds
  key: data

Example usage for the Python API:

from kedro_datasets.pandas import HDFDataset
import pandas as pd

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

dataset = HDFDataset(filepath=tmp_path / "test.h5", key="data")
dataset.save(data)
reloaded = dataset.load()
assert data.equals(reloaded)

Attributes

DEFAULT_FS_ARGS

DEFAULT_LOAD_ARGS

DEFAULT_SAVE_ARGS

Methods

exists()

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

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

Create a dataset 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.

to_config()

Converts the dataset instance into a dictionary-based configuration for serialization.
DEFAULT_FS_ARGS: dict[str, Any] = {'open_args_save': {'mode': 'wb'}}
DEFAULT_LOAD_ARGS: dict[str, Any] = {}
DEFAULT_SAVE_ARGS: dict[str, Any] = {}
__init__(*, filepath, key, load_args=None, save_args=None, version=None, credentials=None, fs_args=None, metadata=None)[source]

Creates a new instance of HDFDataset pointing to a concrete hdf file on a specific filesystem.

Parameters:

  • filepath (str) – Filepath in POSIX format to a hdf 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. Note: http(s) doesn’t support versioning.

  • key (str) – Identifier to the group in the HDF store.

  • load_args (Optional[dict[str, Any]]) – PyTables options for loading hdf files. You can find all available arguments at: https://www.pytables.org/usersguide/libref/top_level.html#tables.open_file All defaults are preserved.

  • save_args (Optional[dict[str, Any]]) – PyTables options for saving hdf files. You can find all available arguments at: https://www.pytables.org/usersguide/libref/top_level.html#tables.open_file All defaults are preserved.

  • 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 open_args_save mode, which is set wb when saving.

  • metadata (Optional[dict[str, Any]]) – Any arbitrary metadata. This is ignored by Kedro, but may be consumed by users or external plugins.

exists()[source]

Checks whether a dataset’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)[source]

Create a dataset 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 dataset is versioned. Has no effect on the dataset if versioning was not enabled.

  • save_version (Optional[str]) – Version string to be used for save operation if the dataset is versioned. Has no effect on the dataset if versioning was not enabled.

Return type:

AbstractDataset

Returns:

An instance of an AbstractDataset subclass.

Raises:

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

load()[source]

Loads data by delegation to the provided load method.

Return type:

DataFrame

Returns:

Data returned by the provided load method.

Raises:

DatasetError – When underlying load method raises error.

release()[source]

Release any cached data.

Raises:

DatasetError – when underlying release method raises error.

Return type:

None

resolve_load_version()[source]

Compute the version the dataset should be loaded with.

Return type:

Optional[str]

resolve_save_version()[source]

Compute the version the dataset should be saved with.

Return type:

Optional[str]

save(data)[source]

Saves data by delegation to the provided save method.

Parameters:

data (DataFrame) – 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

to_config()[source]

Converts the dataset instance into a dictionary-based configuration for serialization. Ensures that any subclass-specific details are handled, with additional logic for versioning and caching implemented for CachedDataset.

Adds a key for the dataset’s type using its module and class name and includes the initialization arguments.

For CachedDataset it extracts the underlying dataset’s configuration, handles the versioned flag and removes unnecessary metadata. It also ensures the embedded dataset’s configuration is appropriately flattened or transformed.

If the dataset has a version key, it sets the versioned flag in the configuration.

Removes the metadata key from the configuration if present.

Return type:

dict[str, Any]

Returns:

A dictionary containing the dataset’s type and initialization arguments.