pasteur.kedro.dataset.auto.AutoDataset

class pasteur.kedro.dataset.auto.AutoDataset(filepath, load_args=None, save_args=None, version=None, credentials=None, fs_args=None, metadata=None)

Modified kedro parquet dataset that acts similarly to a partitioned dataset and implements lazy loading.

In the future, this dataset will automatically handle pickling, pyarrow Tables, DataFrames, and Tensors automatically based on what is saved.

save() data can be a table, a callable, or a dictionary combination of both.

If its a table or a callable, this class acts exactly as ParquetDataset. If its a dictionary, each callable function is called and saved in parallel in a different parquet file, making the provided path a directory. Parallelism is achieved by using Pasteur’s common process pool.

load() returns a dictionary with parquet file names and callables that will load each one. In addition, load() will include an entry _all that will load and concatenate all partitions, with memory optimisations. If save() was called with a single dataframe/callable, then load() will return a callable instead. All callables can receive as input the columns they want to be loaded from the dataframe.

Attributes

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.
reset()
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] = {'index': True}

exists()

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)

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()

Loads data by delegation to the provided load method.

Return type:

LazyDataset[DataFrame]

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

reset()

resolve_load_version()

Compute the version the dataset should be loaded with.

Return type:

str | None

resolve_save_version()

Compute the version the dataset should be saved with.

Return type:

str | None

save(data)

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