kedro_datasets.pandas.GBQQueryDataset

class kedro_datasets.pandas.GBQQueryDataset(sql=None, project=None, credentials=None, load_args=None, fs_args=None, filepath=None, metadata=None)

GBQQueryDataset loads data from a provided SQL query from Google BigQuery. It uses pandas_gbq.read_gbq which itself uses pandas-gbq internally to read from BigQuery table. Therefore it supports all allowed pandas options on read_gbq.

Example adding a catalog entry with the YAML API:

vehicles:
  type: pandas.GBQQueryDataset
  sql: "select shuttle, shuttle_id from spaceflights.shuttles;"
  project: my-project
  credentials: gbq-creds
  load_args:
    reauth: True

Example using Python API:

from kedro_datasets.pandas import GBQQueryDataset

sql = "SELECT * FROM dataset_1.table_a"

dataset = GBQQueryDataset(sql, project="my-project")

sql_data = dataset.load()

Attributes

DEFAULT_LOAD_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()	Loads data by delegation to the provided load method.
release()	Release any cached data.
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_LOAD_ARGS: dict[str, Any] = {}

__init__(sql=None, project=None, credentials=None, load_args=None, fs_args=None, filepath=None, metadata=None)

Creates a new instance of GBQQueryDataset.

Parameters:

• sql (Optional[str]) – The sql query statement.

• project (Optional[str]) – Google BigQuery Account project ID. Optional when available from the environment. https://cloud.google.com/resource-manager/docs/creating-managing-projects

• credentials (Union[dict[str, Any], Credentials, None]) – Credentials for accessing Google APIs. Either a credential that bases on google.auth.credentials.Credentials OR a service account json as a dictionary OR a path to a service account key json file. https://googleapis.dev/python/google-auth/latest/

• load_args (Optional[dict[str, Any]]) – Pandas options for loading BigQuery table into DataFrame. Here you can find all available arguments: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_gbq.html All defaults are preserved.

• fs_args (Optional[dict[str, Any]]) – Extra arguments to pass into underlying filesystem class constructor (e.g. {“project”: “my-project”} for GCSFileSystem) used for reading the SQL query from filepath.

• filepath (Optional[str]) – A path to a file with a sql query statement.

• metadata (Optional[dict[str, Any]]) – Any arbitrary metadata. This is ignored by Kedro, but may be consumed by users or external plugins.

Raises:

DatasetError – When sql and filepath parameters are either both empty or both provided, as well as when the save() method is invoked.

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()

Loads data by delegation to the provided load method.

Return type:

DataFrame

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 (None) – 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:

NoReturn

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.