kedro_datasets.matplotlib.MatplotlibDataset

class kedro_datasets.matplotlib.MatplotlibDataset(*, filepath, fs_args=None, credentials=None, save_args=None, version=None, overwrite=False, metadata=None)

MatplotlibDataset saves one or more Matplotlib objects as image files to an underlying filesystem (e.g. local, S3, GCS).

Example usage for the YAML API:

output_plot:
  type: matplotlib.MatplotlibDataset
  filepath: data/08_reporting/output_plot.png
  save_args:
    format: png

Example usage for the Python API:

 import matplotlib.pyplot as plt
 from kedro_datasets.matplotlib import MatplotlibDataset

 fig = plt.figure()
 plt.plot([1, 2, 3])  
[<matplotlib.lines.Line2D object at 0x...>]
 plot_dataset = MatplotlibDataset(filepath=tmp_path / "data/08_reporting/output_plot.png")
 plt.close()
 plot_dataset.save(fig)

Example saving a plot as a PDF file:

 import matplotlib.pyplot as plt
 from kedro_datasets.matplotlib import MatplotlibDataset

 fig = plt.figure()
 plt.plot([1, 2, 3])  
[<matplotlib.lines.Line2D object at 0x...>]
 pdf_plot_dataset = MatplotlibDataset(
...     filepath=tmp_path / "data/08_reporting/output_plot.pdf", save_args={"format": "pdf"}
... )
 plt.close()
 pdf_plot_dataset.save(fig)

Example saving multiple plots in a folder, using a dictionary:

 import matplotlib.pyplot as plt
 from kedro_datasets.matplotlib import MatplotlibDataset

 plots_dict = {}
 for colour in ["blue", "green", "red"]:
...     plots_dict[f"{colour}.png"] = plt.figure()
...     plt.plot([1, 2, 3], color=colour)
...
[<matplotlib.lines.Line2D object at 0x...>]
[<matplotlib.lines.Line2D object at 0x...>]
[<matplotlib.lines.Line2D object at 0x...>]
 plt.close("all")
 dict_plot_dataset = MatplotlibDataset(filepath=tmp_path / "data/08_reporting/plots")
 dict_plot_dataset.save(plots_dict)

Example saving multiple plots in a folder, using a list:

 import matplotlib.pyplot as plt
 from kedro_datasets.matplotlib import MatplotlibDataset

 plots_list = []
 for i in range(5):  
...     plots_list.append(plt.figure())
...     plt.plot([i, i + 1, i + 2])
...
[<matplotlib.lines.Line2D object at 0x...>]
[<matplotlib.lines.Line2D object at 0x...>]
[<matplotlib.lines.Line2D object at 0x...>]
[<matplotlib.lines.Line2D object at 0x...>]
[<matplotlib.lines.Line2D object at 0x...>]
 plt.close("all")
 list_plot_dataset = MatplotlibDataset(filepath=tmp_path / "data/08_reporting/plots")
 list_plot_dataset.save(plots_list)

Attributes

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()	Loading is not supported for MatplotlibDataset.
preview()	Generates a preview of the matplotlib dataset as a base64 encoded image.
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_SAVE_ARGS: dict[str, Any] = {}

__init__(*, filepath, fs_args=None, credentials=None, save_args=None, version=None, overwrite=False, metadata=None)

Creates a new instance of MatplotlibDataset.

Parameters:

• filepath (str) – Filepath in POSIX format to save Matplotlib objects to, 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.

• 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 key 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 to wb when saving.

• credentials (Optional[dict[str, Any]]) – Credentials required to get access to the underlying filesystem. E.g. for S3FileSystem it should look like: {‘key’: ‘<id>’, ‘secret’: ‘<key>’}}

• save_args (Optional[dict[str, Any]]) – Save args passed to plt.savefig. See https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html

• 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.

• overwrite (bool) – If True, any existing image files will be removed. Only relevant when saving multiple Matplotlib objects at once.

• metadata (Optional[dict[str, Any]]) – Any arbitrary 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 (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()

Loading is not supported for MatplotlibDataset.

Raises:

DatasetError – When called with any arguments.

Return type:

NoReturn

Returns:

Never returns as it always raises an exception.

preview()

Generates a preview of the matplotlib dataset as a base64 encoded image.

Returns:

A base64 encoded string representing the matplotlib plot image.

Return type:

str

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 (Figure | list[Figure] | dict[str, Figure]) – 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.