kedro.io.PartitionedDataSet¶
-
class
kedro.io.
PartitionedDataSet
(path, dataset, filepath_arg='filepath', filename_suffix='', credentials=None, load_args=None, fs_args=None)[source]¶ Bases:
kedro.io.core.AbstractDataSet
PartitionedDataSet
loads and saves partitioned file-like data using the underlying dataset definition. For filesystem level operations it uses fsspec: https://github.com/intake/filesystem_spec.Example:
import pandas as pd from kedro.io import PartitionedDataSet # these credentials will be passed to both 'fsspec.filesystem()' call # and the dataset initializer credentials = {"key1": "secret1", "key2": "secret2"} data_set = PartitionedDataSet( path="s3://bucket-name/path/to/folder", dataset="CSVDataSet", credentials=credentials ) loaded = data_set.load() # assert isinstance(loaded, dict) combine_all = pd.DataFrame() for partition_id, partition_load_func in loaded.items(): partition_data = partition_load_func() combine_all = pd.concat( [combine_all, partition_data], ignore_index=True, sort=True ) new_data = pd.DataFrame({"new": [1, 2]}) # creates "s3://bucket-name/path/to/folder/new/partition.csv" data_set.save({"new/partition.csv": new_data})
Methods
PartitionedDataSet.__init__
(path, dataset[, …])Creates a new instance of PartitionedDataSet
.PartitionedDataSet.exists
()Checks whether a data set’s output already exists by calling the provided _exists() method. PartitionedDataSet.from_config
(name, config)Create a data set instance using the configuration provided. PartitionedDataSet.load
()Loads data by delegation to the provided load method. PartitionedDataSet.release
()Release any cached data. PartitionedDataSet.save
(data)Saves data by delegation to the provided save method. -
__init__
(path, dataset, filepath_arg='filepath', filename_suffix='', credentials=None, load_args=None, fs_args=None)[source]¶ Creates a new instance of
PartitionedDataSet
.Parameters: - path (
str
) – Path to the folder containing partitioned data. If path starts with the protocol (e.g.,s3://
) then the correspondingfsspec
concrete filesystem implementation will be used. If protocol is not specified,fsspec.implementations.local.LocalFileSystem
will be used. Note: Some concrete implementations are bundled withfsspec
, while others (likes3
orgcs
) must be installed separately prior to usage of thePartitionedDataSet
. - dataset (
Union
[str
,Type
[AbstractDataSet
],Dict
[str
,Any
]]) – Underlying dataset definition. This is used to instantiate the dataset for each file located inside thepath
. Accepted formats are: a) object of a class that inherits fromAbstractDataSet
b) a string representing a fully qualified class name to such class c) a dictionary withtype
key pointing to a string from b), other keys are passed to the Dataset initializer. Credentials for the dataset can be explicitly specified in this configuration. - filepath_arg (
str
) – Underlying dataset initializer argument that will contain a path to each corresponding partition file. If unspecified, defaults to “filepath”. - filename_suffix (
str
) – If specified, only partitions that end with this string will be processed. - credentials (
Optional
[Dict
[str
,Any
]]) – Protocol-specific options that will be passed tofsspec.filesystem
https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.filesystem and the dataset initializer. If the dataset config contains explicit credentials spec, then such spec will take precedence. All possible credentials management scenarios are documented here: https://kedro.readthedocs.io/en/stable/04_user_guide/08_advanced_io.html#partitioned-dataset-credentials - load_args (
Optional
[Dict
[str
,Any
]]) – Keyword arguments to be passed intofind()
method of the filesystem implementation. - fs_args (
Optional
[Dict
[str
,Any
]]) – Extra arguments to pass into underlying filesystem class constructor (e.g. {“project”: “my-project”} forGCSFileSystem
)
Raises: DataSetError
– If versioning is enabled for the underlying dataset.- path (
-
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 forload
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 forsave
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.- name (
-
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.Return type: None
-