kedro.io.DataCatalog¶
- class kedro.io.DataCatalog(datasets=None, feed_dict=None, dataset_patterns=None, load_versions=None, save_version=None)[source]¶
DataCatalog
stores instances ofAbstractDataset
implementations to provideload
andsave
capabilities from anywhere in the program. To use aDataCatalog
, you need to instantiate it with a dictionary of data sets. Then it will act as a single point of reference for your calls, relaying load and save functions to the underlying data sets.Methods
add
(dataset_name, dataset[, replace])Adds a new
AbstractDataset
object to theDataCatalog
.add_all
(datasets[, 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.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, ...])Create a
DataCatalog
instance from configuration.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
([extra_dataset_patterns])Returns a shallow copy of the current object.
- __init__(datasets=None, feed_dict=None, dataset_patterns=None, load_versions=None, save_version=None)[source]¶
DataCatalog
stores instances ofAbstractDataset
implementations to provideload
andsave
capabilities from anywhere in the program. To use aDataCatalog
, you need to instantiate it with a dictionary of data sets. Then it will act as a single point of reference for your calls, relaying load and save functions to the underlying data sets.- Parameters:
datasets (dict[str, AbstractDataset] | None) – A dictionary of data set names and data set instances.
feed_dict (dict[str, Any] | None) – A feed dict with data to be added in memory.
dataset_patterns (Patterns | None) – A dictionary of data set factory patterns and corresponding data set configuration. When fetched from catalog configuration these patterns will be sorted by: 1. Decreasing specificity (number of characters outside the curly brackets) 2. Decreasing number of placeholders (number of curly bracket pairs) 3. Alphabetically A pattern of specificity 0 is a catch-all pattern and will overwrite the default pattern provided through the runners if it comes before “default” in the alphabet. Such an overwriting pattern will emit a warning. The “{default}” name will not emit a warning.
load_versions (dict[str, str] | None) – A mapping between data set names and versions to load. Has no effect on data sets without enabled versioning.
save_version (str | None) – Version string to be used for
save
operations by all data sets with enabled versioning. It must: a) be a case-insensitive string that conforms with operating system filename limitations, b) always return the latest version when sorted in lexicographical order.
Example:
from kedro_datasets.pandas import CSVDataset cars = CSVDataset(filepath="cars.csv", load_args=None, save_args={"index": False}) io = DataCatalog(datasets={'cars': cars})
- add(dataset_name, dataset, replace=False)[source]¶
Adds a new
AbstractDataset
object to theDataCatalog
.- Parameters:
dataset_name (
str
) – A unique data set name which has not been registered yet.dataset (
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_datasets.pandas import CSVDataset io = DataCatalog(datasets={ 'cars': CSVDataset(filepath="cars.csv") }) io.add("boats", CSVDataset(filepath="boats.csv"))
- Return type:
- add_all(datasets, replace=False)[source]¶
Adds a group of new data sets to the
DataCatalog
.- Parameters:
datasets (
dict
[str
,AbstractDataset
]) – A dictionary of dataset names and dataset 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_datasets.pandas import CSVDataset, ParquetDataset io = DataCatalog(datasets={ "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:
- add_feed_dict(feed_dict, replace=False)[source]¶
Adds instances of
MemoryDataset
, containing the data provided through feed_dict.- Parameters:
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:
- confirm(name)[source]¶
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:
- exists(name)[source]¶
Checks whether registered data set exists by calling its exists() method. Raises a warning and returns False if exists() is not implemented.
- classmethod from_config(catalog, credentials=None, load_versions=None, save_version=None)[source]¶
Create a
DataCatalog
instance from configuration. This is a factory method used to provide developers with a way to instantiateDataCatalog
with configuration parsed from configuration files.- Parameters:
catalog (dict[str, dict[str, Any]] | None) – A dictionary whose keys are the data set names and the values are dictionaries with the constructor arguments for classes implementing
AbstractDataset
. The data set class to be loaded is specified with the keytype
and their fully qualified class name. Allkedro.io
data set can be specified by their class name only, i.e. their module name can be omitted.credentials (dict[str, dict[str, Any]] | None) – A dictionary containing credentials for different data sets. Use the
credentials
key in aAbstractDataset
to refer to the appropriate credentials as shown in the example below.load_versions (dict[str, str] | None) – A mapping between dataset names and versions to load. Has no effect on data sets without enabled versioning.
save_version (str | None) – Version string to be used for
save
operations by all data sets with enabled versioning. It must: a) be a case-insensitive string that conforms with operating system filename limitations, b) always return the latest version when sorted in lexicographical order.
- Return type:
- Returns:
An instantiated
DataCatalog
containing all specified data sets, created and ready to use.- Raises:
DatasetError – When the method fails to create any of the data sets from their config.
DatasetNotFoundError – When load_versions refers to a dataset that doesn’t exist in the catalog.
Example:
config = { "cars": { "type": "pandas.CSVDataset", "filepath": "cars.csv", "save_args": { "index": False } }, "boats": { "type": "pandas.CSVDataset", "filepath": "s3://aws-bucket-name/boats.csv", "credentials": "boats_credentials", "save_args": { "index": False } } } credentials = { "boats_credentials": { "client_kwargs": { "aws_access_key_id": "<your key id>", "aws_secret_access_key": "<your secret>" } } } catalog = DataCatalog.from_config(config, credentials) df = catalog.load("cars") catalog.save("boats", df)
- list(regex_search=None)[source]¶
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 (str | None) – An optional regular expression which can be provided to limit the data sets returned by a particular pattern.
- Return type:
- 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:
- Return type:
Any
- Returns:
The loaded data as configured.
- Raises:
DatasetNotFoundError – When a data set with the given name has not yet been registered.
Example:
from kedro.io import DataCatalog from kedro_datasets.pandas import CSVDataset cars = CSVDataset(filepath="cars.csv", load_args=None, save_args={"index": False}) io = DataCatalog(datasets={'cars': cars}) df = io.load("cars")
- release(name)[source]¶
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.
- Return type:
- save(name, data)[source]¶
Save data to a registered data set.
- Parameters:
- Raises:
DatasetNotFoundError – When a data set with the given name has not yet been registered.
Example:
import pandas as pd from kedro_datasets.pandas import CSVDataset cars = CSVDataset(filepath="cars.csv", load_args=None, save_args={"index": False}) io = DataCatalog(datasets={'cars': cars}) df = pd.DataFrame({'col1': [1, 2], 'col2': [4, 5], 'col3': [5, 6]}) io.save("cars", df)
- Return type: