kedro_datasets.geopandas.GenericDataset

class kedro_datasets.geopandas.GenericDataset(*, filepath, file_format='file', load_args=None, save_args=None, version=None, credentials=None, fs_args=None, metadata=None)

GenericDataset loads/saves data to a file using an underlying filesystem (eg: local, S3, GCS). The underlying functionality is supported by geopandas, so it supports all allowed geopandas (pandas) options for loading and saving files.

Example:

 import geopandas as gpd
 from kedro_datasets.geopandas import GenericDataset
 from shapely.geometry import Point

 data = gpd.GeoDataFrame(
...     {"col1": [1, 2], "col2": [4, 5], "col3": [5, 6]},
...     geometry=[Point(1, 1), Point(2, 4)],
... )
 dataset = GenericDataset(filepath=tmp_path / "test.geojson")
 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.
invalidate_cache()	Invalidate underlying filesystem cache.
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, file_format='file', load_args=None, save_args=None, version=None, credentials=None, fs_args=None, metadata=None)

Creates a new instance of GenericDataset pointing to a concrete file on a specific filesystem fsspec.

Parameters:

• filepath (str) – Filepath in POSIX format to a 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.

• file_format (str) – String which is used to match the appropriate load/save method on a best effort basis. For example if ‘parquet’ is passed in the geopandas.read_parquet and geopandas.DataFrame.to_parquet will be identified. An error will be raised unless at least one matching read_{file_format} or to_{file_format} method is identified. Defaults to ‘file’.

• load_args (Optional[dict[str, Any]]) – GeoPandas options for loading files. Here you can find all available arguments: https://geopandas.org/en/stable/docs/reference/api/geopandas.read_file.html

• save_args (Optional[dict[str, Any]]) – GeoPandas options for saving files. Here you can find all available arguments: https://geopandas.org/en/stable/docs/reference/api/geopandas.GeoDataFrame.to_file.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

• credentials (Optional[dict[str, Any]]) – credentials required to access the underlying filesystem. Eg. for GCFileSystem it would 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 to 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()

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.

invalidate_cache()

Invalidate underlying filesystem cache.

Return type:

None

load()

Loads data by delegation to the provided load method.

Return type:

GeoDataFrame | dict[str, GeoDataFrame]

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 (GeoDataFrame) – 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.