kedro_datasets.pandas.GBQTableDataset

class kedro_datasets.pandas.GBQTableDataset(*, dataset, table_name, project=None, credentials=None, load_args=None, save_args=None, metadata=None)

GBQTableDataset loads and saves data from/to Google BigQuery. It uses pandas-gbq to read and write from/to BigQuery table.

Example usage for the YAML API:

vehicles:
  type: pandas.GBQTableDataset
  dataset: big_query_dataset
  table_name: big_query_table
  project: my-project
  credentials: gbq-creds
  load_args:
    reauth: True
  save_args:
    chunk_size: 100

Example usage for the Python API:

 from kedro_datasets.pandas import GBQTableDataset
 import pandas as pd

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

 dataset = GBQTableDataset(
...     dataset="dataset", table_name="table_name", project="my-project"
... )
 dataset.save(data)
 reloaded = dataset.load()

 assert data.equals(reloaded)

Attributes

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.
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] = {}

DEFAULT_SAVE_ARGS: dict[str, Any] = {'progress_bar': False}

__init__(*, dataset, table_name, project=None, credentials=None, load_args=None, save_args=None, metadata=None)

Creates a new instance of GBQTableDataset.

Parameters:

• dataset (str) – Google BigQuery dataset.

• table_name (str) – Google BigQuery table name.

• 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], str, 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.

• save_args (Optional[dict[str, Any]]) – Pandas options for saving DataFrame to BigQuery table. Here you can find all available arguments: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_gbq.html All defaults are preserved, but “progress_bar”, which is set to False.

• 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 load_args['location'] and save_args['location'] are different.

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 (DataFrame) – 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.