kedro.io.MemoryDataset

class kedro.io.MemoryDataset(data=<object object>, copy_mode=None, metadata=None)

MemoryDataset loads and saves data from/to an in-memory Python object. The _EPHEMERAL attribute is set to True to indicate MemoryDataset’s non-persistence.

Example:

from kedro.io import MemoryDataset
import pandas as pd

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

loaded_data = dataset.load()
assert loaded_data.equals(data)

new_data = pd.DataFrame({'col1': [1, 2], 'col2': [4, 5]})
dataset.save(new_data)
reloaded_data = dataset.load()
assert reloaded_data.equals(new_data)

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.
save(data)	Saves data by delegation to the provided save method.
to_config()	Converts the dataset instance into a dictionary-based configuration for serialization.

__init__(data=<object object>, copy_mode=None, metadata=None)

Creates a new instance of MemoryDataset pointing to the provided Python object.

Parameters:

• data (Any) – Python object containing the data.

• copy_mode (str | None) – The copy mode used to copy the data. Possible values are: “deepcopy”, “copy” and “assign”. If not provided, it is inferred based on the data type.

• metadata (dict[str, Any] | None) – Any arbitrary metadata. This is ignored by Kedro, but may be consumed by users or external plugins.

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 (str | None) – 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 (str | None) – 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:

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

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

to_config()

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.