Class TabularDataset (1.31.1)

Version 1.31.1

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TabularDataset(
    dataset_name: str,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
)

Managed tabular dataset resource for Vertex AI.

Properties

column_names

Retrieve the columns for the dataset by extracting it from the Google Cloud Storage or Google BigQuery source.

Exceptions
Type	Description
RuntimeError	When no valid source is found.

create_time

Time this resource was created.

display_name

Display name of this resource.

encryption_spec

Customer-managed encryption key options for this Vertex AI resource.

If this is set, then all resources created by this Vertex AI resource will be encrypted with the provided encryption key.

gca_resource

The underlying resource proto representation.

labels

User-defined labels containing metadata about this resource.

Read more about labels at https://goo.gl/xmQnxf

metadata_schema_uri

The metadata schema uri of this dataset resource.

name

Name of this resource.

resource_name

Full qualified resource name.

update_time

Time this resource was last updated.

Methods

TabularDataset

TabularDataset(
    dataset_name: str,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
)

Retrieves an existing managed dataset given a dataset name or ID.

Parameters
Name	Description
dataset_name	str Required. A fully-qualified dataset resource name or dataset ID. Example: "projects/123/locations/us-central1/datasets/456" or "456" when project and location are initialized or passed.
project	str Optional project to retrieve dataset from. If not set, project set in aiplatform.init will be used.
location	str Optional location to retrieve dataset from. If not set, location set in aiplatform.init will be used.
credentials	auth_credentials.Credentials Custom credentials to use to retrieve this Dataset. Overrides credentials set in aiplatform.init.

create

create(
    display_name: typing.Optional[str] = None,
    gcs_source: typing.Optional[typing.Union[str, typing.Sequence[str]]] = None,
    bq_source: typing.Optional[str] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
    request_metadata: typing.Optional[typing.Sequence[typing.Tuple[str, str]]] = (),
    labels: typing.Optional[typing.Dict[str, str]] = None,
    encryption_spec_key_name: typing.Optional[str] = None,
    sync: bool = True,
    create_request_timeout: typing.Optional[float] = None,
) -> google.cloud.aiplatform.datasets.tabular_dataset.TabularDataset

Creates a new tabular dataset.

Parameters
Name	Description
display_name	str Optional. The user-defined name of the Dataset. The name can be up to 128 characters long and can be consist of any UTF-8 characters.
gcs_source	Union[str, Sequence[str]] Google Cloud Storage URI(-s) to the input file(s). .. rubric:: Examples str: "gs://bucket/file.csv" Sequence[str]: ["gs://bucket/file1.csv", "gs://bucket/file2.csv"]
bq_source	str BigQuery URI to the input table. .. rubric:: Example "bq://project.dataset.table_name"
project	str Project to upload this dataset to. Overrides project set in aiplatform.init.
location	str Location to upload this dataset to. Overrides location set in aiplatform.init.
credentials	auth_credentials.Credentials Custom credentials to use to upload this dataset. Overrides credentials set in aiplatform.init.
request_metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.
labels	Dict[str, str] Optional. Labels with user-defined metadata to organize your Tensorboards. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Tensorboard (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
encryption_spec_key_name	Optional[str] Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the dataset. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created. If set, this Dataset and all sub-resources of this Dataset will be secured by this key. Overrides encryption_spec_key_name set in aiplatform.init.
sync	bool Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.
create_request_timeout	float Optional. The timeout for the create request in seconds.

Returns
Type	Description
tabular_dataset (TabularDataset)	Instantiated representation of the managed tabular dataset resource.

create_from_dataframe

create_from_dataframe(
    df_source: pd.DataFrame,
    staging_path: str,
    bq_schema: typing.Optional[
        typing.Union[str, google.cloud.bigquery.schema.SchemaField]
    ] = None,
    display_name: typing.Optional[str] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
) -> TabularDataset

Creates a new tabular dataset from a Pandas DataFrame.

Parameters
Name	Description
staging_path	str Required. The BigQuery table to stage the data for Vertex. Because Vertex maintains a reference to this source to create the Vertex Dataset, this BigQuery table should not be deleted. Example: bq://my-project.my-dataset.my-table. If the provided BigQuery table doesn't exist, this method will create the table. If the provided BigQuery table already exists, and the schemas of the BigQuery table and your DataFrame match, this method will append the data in your local DataFrame to the table. The location of the provided BigQuery table should conform to the location requirements specified here: https://cloud.google.com/vertex-ai/docs/general/locations#bq-locations.
bq_schema	Optional[Union[str, bigquery.SchemaField]] Optional. If not set, BigQuery will autodetect the schema using your DataFrame's column types. If set, BigQuery will use the schema you provide when creating the staging table. For more details, see: https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.job.LoadJobConfig#google_cloud_bigquery_job_LoadJobConfig_schema
display_name	str Optional. The user-defined name of the Dataset. The name can be up to 128 characters long and can be consist of any UTF-8 charact
project	str Optional. Project to upload this dataset to. Overrides project set in aiplatform.init.
location	str Optional. Location to upload this dataset to. Overrides location set in aiplatform.init.
credentials	auth_credentials.Credentials Optional. Custom credentials to use to upload this dataset. Overrides credentials set in aiplatform.init.
df_source	pd.DataFrame Required. Pandas DataFrame containing the source data for ingestion as a TabularDataset. This method will use the data types from the provided DataFrame when creating the dataset.

Returns
Type	Description
tabular_dataset (TabularDataset)	Instantiated representation of the managed tabular dataset resource.

delete

delete(sync: bool = True) -> None

Deletes this Vertex AI resource. WARNING: This deletion is permanent.

Parameter
Name	Description
sync	bool Whether to execute this deletion synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

export_data

export_data(output_dir: str) -> typing.Sequence[str]

Exports data to output dir to GCS.

Parameter
Name	Description
output_dir	str Required. The Google Cloud Storage location where the output is to be written to. In the given directory a new directory will be created with name: export-data- where timestamp is in YYYYMMDDHHMMSS format. All export output will be written into that directory. Inside that directory, annotations with the same schema will be grouped into sub directories which are named with the corresponding annotations' schema title. Inside these sub directories, a schema.yaml will be created to describe the output format. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.

Returns
Type	Description
exported_files (Sequence[str])	All of the files that are exported in this export operation.

import_data

import_data()

Upload data to existing managed dataset.

Parameters
Name	Description
gcs_source	Union[str, Sequence[str]] Required. Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames. .. rubric:: Examples str: "gs://bucket/file.csv" Sequence[str]: ["gs://bucket/file1.csv", "gs://bucket/file2.csv"]
import_schema_uri	str Required. Points to a YAML file stored on Google Cloud Storage describing the import format. Validation will be done against the schema. The schema is defined as an OpenAPI 3.0.2 Schema Object https://tinyurl.com/y538mdwt__.
data_item_labels	Dict Labels that will be applied to newly imported DataItems. If an identical DataItem as one being imported already exists in the Dataset, then these labels will be appended to these of the already existing one, and if labels with identical key is imported before, the old label value will be overwritten. If two DataItems are identical in the same import data operation, the labels will be combined and if key collision happens in this case, one of the values will be picked randomly. Two DataItems are considered identical if their content bytes are identical (e.g. image bytes or pdf bytes). These labels will be overridden by Annotation labels specified inside index file referenced by import_schema_uri, e.g. jsonl file. This arg is not for specifying the annotation name or the training target of your data, but for some global labels of the dataset. E.g., 'data_item_labels={"aiplatform.googleapis.com/ml_use":"training"}' specifies that all the uploaded data are used for training.
sync	bool Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.
import_request_timeout	float Optional. The timeout for the import request in seconds.

Returns
Type	Description
dataset (Dataset)	Instantiated representation of the managed dataset resource.

list

list(
    filter: typing.Optional[str] = None,
    order_by: typing.Optional[str] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
) -> typing.List[google.cloud.aiplatform.base.VertexAiResourceNoun]

List all instances of this Dataset resource.

Example Usage:

aiplatform.TabularDataset.list( filter='labels.my_key="my_value"', order_by='display_name' )

Parameters
Name	Description
filter	str Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported.
order_by	str Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: display_name, create_time, update_time
project	str Optional. Project to retrieve list from. If not set, project set in aiplatform.init will be used.
location	str Optional. Location to retrieve list from. If not set, location set in aiplatform.init will be used.
credentials	auth_credentials.Credentials Optional. Custom credentials to use to retrieve list. Overrides credentials set in aiplatform.init.

to_dict

to_dict() -> typing.Dict[str, typing.Any]

Returns the resource proto as a dictionary.

update

update(
    *,
    display_name: typing.Optional[str] = None,
    labels: typing.Optional[typing.Dict[str, str]] = None,
    description: typing.Optional[str] = None,
    update_request_timeout: typing.Optional[float] = None
) -> google.cloud.aiplatform.datasets.dataset._Dataset

Update the dataset. Updatable fields:

• display_name
• description
• labels

Parameters
Name	Description
display_name	str Optional. The user-defined name of the Dataset. The name can be up to 128 characters long and can be consist of any UTF-8 characters.
labels	Dict[str, str] Optional. Labels with user-defined metadata to organize your Tensorboards. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Tensorboard (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
description	str Optional. The description of the Dataset.
update_request_timeout	float Optional. The timeout for the update request in seconds.

Returns
Type	Description
dataset (Dataset)	Updated dataset.

wait

wait()

Helper method that blocks until all futures are complete.