kedro.io.DataCatalogWithDefault¶

class kedro.io.DataCatalogWithDefault(data_sets=None, default=None, remember=False)[source]¶

A DataCatalog with a default DataSet implementation for any data set which is not registered in the catalog.

Methods

`add`(data_set_name, data_set[, replace])	Adds a new `AbstractDataSet` object to the `DataCatalog`.
`add_all`(data_sets[, replace])	Adds a group of new data sets to the `DataCatalog`.
`add_feed_dict`(feed_dict[, replace])	Adds instances of `MemoryDataSet`, containing the data provided through feed_dict.
`add_transformer`(transformer[, data_set_names])	Add a `DataSet` Transformer to the:class:~kedro.io.DataCatalog.
`confirm`(name)	Confirm a dataset by its name.
`exists`(name)	Checks whether registered data set exists by calling its exists() method.
`from_config`(catalog[, credentials, …])	To create a `DataCatalogWithDefault` from configuration, please use: .
`from_data_catalog`(data_catalog, default)	Convenience factory method to create a `DataCatalogWithDefault` from a `DataCatalog`
`list`([regex_search])	List of all `DataSet` names registered in the catalog.
`load`(name[, version])	Loads a registered data set
`release`(name)	Release any cached data associated with a data set
`save`(name, data)	Save data to a registered data set.
`shallow_copy`()	Returns a shallow copy of the current object.

__init__(data_sets=None, default=None, remember=False)[source]¶

DataCatalogWithDefault is deprecated and will be removed in Kedro 0.18.0. A DataCatalog with a default DataSet implementation for any data set which is not registered in the catalog.

Parameters

data_sets (Optional[Dict[str, AbstractDataSet]]) – A dictionary of data set names and data set instances.
default (Optional[Callable[[str], AbstractDataSet]]) – A callable which accepts a single argument of type string, the key of the data set, and returns an AbstractDataSet. load and save calls on data sets which are not registered to the catalog will be delegated to this AbstractDataSet.
remember (bool) – If True, then store in the catalog any AbstractDataSets provided by the default callable argument. Useful when one want to transition from a DataCatalogWithDefault to a DataCatalog: just call DataCatalogWithDefault.to_yaml, after all required data sets have been saved/loaded, and use the generated YAML file with a new DataCatalog.

Raises

TypeError – If default is not a callable.

Example:

from kedro.extras.datasets.pandas import CSVDataSet

def default_data_set(name):
    return CSVDataSet(filepath='data/01_raw/' + name)

io = DataCatalog(data_sets={},
                 default=default_data_set)

# load the file in data/raw/cars.csv
df = io.load("cars.csv")

add(data_set_name, data_set, replace=False)¶

Adds a new AbstractDataSet object to the DataCatalog.

Parameters

data_set_name (str) – A unique data set name which has not been registered yet.
data_set (AbstractDataSet) – A data set object to be associated with the given data set name.
replace (bool) – Specifies whether to replace an existing DataSet with the same name is allowed.

Raises

DataSetAlreadyExistsError – When a data set with the same name has already been registered.

Example:

from kedro.extras.datasets.pandas import CSVDataSet

io = DataCatalog(data_sets={
                  'cars': CSVDataSet(filepath="cars.csv")
                 })

io.add("boats", CSVDataSet(filepath="boats.csv"))

Return type: None

add_all(data_sets, replace=False)¶

Adds a group of new data sets to the DataCatalog.

Parameters

data_sets (Dict[str, AbstractDataSet]) – A dictionary of DataSet names and data set instances.
replace (bool) – Specifies whether to replace an existing DataSet with the same name is allowed.

Raises

DataSetAlreadyExistsError – When a data set with the same name has already been registered.

Example:

from kedro.extras.datasets.pandas import CSVDataSet, ParquetDataSet

io = DataCatalog(data_sets={
                  "cars": CSVDataSet(filepath="cars.csv")
                 })
additional = {
    "planes": ParquetDataSet("planes.parq"),
    "boats": CSVDataSet(filepath="boats.csv")
}

io.add_all(additional)

assert io.list() == ["cars", "planes", "boats"]

Return type: None

add_feed_dict(feed_dict, replace=False)¶

Adds instances of MemoryDataSet, containing the data provided through feed_dict.

Parameters

feed_dict (Dict[str, Any]) – A feed dict with data to be added in memory.
replace (bool) – Specifies whether to replace an existing DataSet with the same name is allowed.

Example:

import pandas as pd

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

io = DataCatalog()
io.add_feed_dict({
    'data': df
}, replace=True)

assert io.load("data").equals(df)

Return type: None

add_transformer(transformer, data_set_names=None)¶

Add a DataSet Transformer to the:class:~kedro.io.DataCatalog. Transformers can modify the way Data Sets are loaded and saved.

Parameters

transformer (AbstractTransformer) – The transformer instance to add.
data_set_names (Union[str, Iterable[str], None]) – The Data Sets to add the transformer to. Or None to add the transformer to all Data Sets.

Raises

DataSetNotFoundError – When a transformer is being added to a non existent data set.
TypeError – When transformer isn’t an instance of AbstractTransformer

confirm(name)¶

Confirm a dataset by its name.

Parameters: name (str) – Name of the dataset.
Raises: DataSetError – When the dataset does not have confirm method.
Return type: None

exists(name)¶

Checks whether registered data set exists by calling its exists() method. Raises a warning and returns False if exists() is not implemented.

Parameters: name (str) – A data set to be checked.
Return type: bool
Returns: Whether the data set output exists.

classmethod from_config(catalog, credentials=None, load_versions=None, save_version=None, journal=None)[source]¶

To create a DataCatalogWithDefault from configuration, please use:

DataCatalogWithDefault.from_data_catalog(
    DataCatalog.from_config(catalog, credentials))

Parameters

catalog (Optional[Dict[str, Dict[str, Any]]]) – See DataCatalog.from_config
credentials (Optional[Dict[str, Dict[str, Any]]]) – See DataCatalog.from_config
load_versions (Optional[Dict[str, str]]) – See DataCatalog.from_config
save_version (Optional[str]) – See DataCatalog.from_config
journal (Optional[Journal]) – See DataCatalog.from_config

Raises

ValueError – If you try to instantiate a DataCatalogWithDefault directly with this method.

classmethod from_data_catalog(data_catalog, default)[source]¶

Convenience factory method to create a DataCatalogWithDefault from a DataCatalog

A DataCatalog with a default DataSet implementation for any data set which is not registered in the catalog.

Parameters

data_catalog (DataCatalog) – The DataCatalog to convert to a DataCatalogWithDefault.
default (Callable[[str], AbstractDataSet]) – A callable which accepts a single argument of type string, the key of the data set, and returns an AbstractDataSet. load and save calls on data sets which are not registered to the catalog will be delegated to this AbstractDataSet.

Return type

DataCatalogWithDefault

Returns

A new DataCatalogWithDefault which contains all the AbstractDataSets from the provided data-catalog.

list(regex_search=None)¶

List of all DataSet names registered in the catalog. This can be filtered by providing an optional regular expression which will only return matching keys.

Parameters: regex_search (Optional[str]) – An optional regular expression which can be provided to limit the data sets returned by a particular pattern.
Return type: List[str]
Returns: A list of DataSet names available which match the regex_search criteria (if provided). All data set names are returned by default.
Raises: SyntaxError – When an invalid regex filter is provided.

Example:

io = DataCatalog()
# get data sets where the substring 'raw' is present
raw_data = io.list(regex_search='raw')
# get data sets which start with 'prm' or 'feat'
feat_eng_data = io.list(regex_search='^(prm|feat)')
# get data sets which end with 'time_series'
models = io.list(regex_search='.+time_series$')

load(name, version=None)[source]¶

Loads a registered data set

Parameters

name (str) – A data set to be loaded.
version (Optional[str]) – Optional version to be loaded.

Return type

Any

Returns

The loaded data as configured.

Raises

DataSetNotFoundError – When a data set with the given name has not yet been registered.

release(name)¶

Release any cached data associated with a data set

Parameters: name (str) – A data set to be checked.
Raises: DataSetNotFoundError – When a data set with the given name has not yet been registered.

save(name, data)[source]¶

Save data to a registered data set.

Parameters

name (str) – A data set to be saved to.
data (Any) – A data object to be saved as configured in the registered data set.

Raises

DataSetNotFoundError – When a data set with the given name has not yet been registered.

shallow_copy()[source]¶: Returns a shallow copy of the current object. :rtype: DataCatalogWithDefault :returns: Copy of the current object.