kedro.extras.datasets.pandas.HDFDataSet¶

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

Bases: kedro.io.core.AbstractVersionedDataSet

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:

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

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

# data_set = HDFDataSet(filepath="gcs://bucket/test.hdf", key='data')
data_set = HDFDataSet(filepath="test.h5", key='data')
data_set.save(data)
reloaded = data_set.load()
assert data.equals(reloaded)

Attributes

`HDFDataSet.DEFAULT_LOAD_ARGS`
`HDFDataSet.DEFAULT_SAVE_ARGS`

Methods

`HDFDataSet.__init__`(filepath, key[, …])	Creates a new instance of `HDFDataSet` pointing to a concrete hdf file on a specific filesystem.
`HDFDataSet.exists`()	Checks whether a data set’s output already exists by calling the provided _exists() method.
`HDFDataSet.from_config`(name, config[, …])	Create a data set instance using the configuration provided.
`HDFDataSet.load`()	Loads data by delegation to the provided load method.
`HDFDataSet.release`()	Release any cached data.
`HDFDataSet.resolve_load_version`()	Compute the version the dataset should be loaded with.
`HDFDataSet.resolve_save_version`()	Compute the version the dataset should be saved with.
`HDFDataSet.save`(data)	Saves data by delegation to the provided save method.

DEFAULT_LOAD_ARGS = {}¶

DEFAULT_SAVE_ARGS = {}¶

__init__(filepath, key, load_args=None, save_args=None, version=None, credentials=None, fs_args=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 mode, which is set wb when saving.

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.

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`