Use the BigQuery Storage API to speed up downloads of large tables to DataFrame.

## Code sample

### Python


Before trying this sample, follow the Python setup instructions in the
[BigQuery quickstart using
client libraries](https://docs.cloud.google.com/bigquery/docs/quickstarts/quickstart-client-libraries).


For more information, see the
[BigQuery Python API
reference documentation](https://docs.cloud.google.com/python/docs/reference/bigquery/latest).


To authenticate to BigQuery, set up Application Default Credentials.
For more information, see

[Set up authentication for client libraries](https://docs.cloud.google.com/bigquery/docs/authentication#client-libs).


    from google.cloud import https://docs.cloud.google.com/python/docs/reference/bigquery/latest

    # Construct a BigQuery client object.
    client = https://docs.cloud.google.com/python/docs/reference/bigquery/latest.https://docs.cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.client.Client.html()

    # TODO(developer): Set table_id to the fully-qualified table ID in standard
    # SQL format, including the project ID and dataset ID.
    table_id = "bigquery-public-data.usa_names.usa_1910_current"

    # Use the BigQuery Storage API to speed-up downloads of large tables.
    dataframe = client.https://docs.cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.client.Client.html#google_cloud_bigquery_client_Client_list_rows(table_id).to_dataframe(create_bqstorage_client=True)

    print(dataframe.info())

## What's next


To search and filter code samples for other Google Cloud products, see the
[Google Cloud sample browser](https://docs.cloud.google.com/docs/samples?product=bigquery).