Run a query on the natality public dataset and write the results to a destination table.

## Explore further


For detailed documentation that includes this code sample, see the following:

- [Use Apache Spark ML for machine learning](https://docs.cloud.google.com/managed-spark/docs/tutorials/bigquery-sparkml)

## 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).

    """Create a Google BigQuery linear regression input table.

    In the code below, the following actions are taken:
    * A new dataset is created "natality_regression."
    * A query is run against the public dataset,
        bigquery-public-data.samples.natality, selecting only the data of
        interest to the regression, the output of which is stored in a new
        "regression_input" table.
    * The output table is moved over the wire to the user's default project via
        the built-in BigQuery Connector for Spark that bridges BigQuery and
        Cloud Dataproc.
    """

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

    # Create a new Google BigQuery client using Google Cloud Platform project
    # defaults.
    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()

    # Prepare a reference to a new dataset for storing the query results.
    dataset_id = "natality_regression"
    dataset_id_full = f"{client.project}.{dataset_id}"

    dataset = https://docs.cloud.google.com/python/docs/reference/bigquery/latest.https://docs.cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.dataset.Dataset.html(dataset_id_full)

    # Create the new BigQuery dataset.
    dataset = client.https://docs.cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.client.Client.html#google_cloud_bigquery_client_Client_create_dataset(dataset)

    # Configure the query job.
    job_config = https://docs.cloud.google.com/python/docs/reference/bigquery/latest.https://docs.cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.job.QueryJobConfig.html()

    # Set the destination table to where you want to store query results.
    # As of google-cloud-bigquery 1.11.0, a fully qualified table ID can be
    # used in place of a TableReference.
    job_config.destination = f"{dataset_id_full}.regression_input"

    # Set up a query in Standard SQL, which is the default for the BigQuery
    # Python client library.
    # The query selects the fields of interest.
    query = """
        SELECT
            weight_pounds, mother_age, father_age, gestation_weeks,
            weight_gain_pounds, apgar_5min
        FROM
            `bigquery-public-data.samples.natality`
        WHERE
            weight_pounds IS NOT NULL
            AND mother_age IS NOT NULL
            AND father_age IS NOT NULL
            AND gestation_weeks IS NOT NULL
            AND weight_gain_pounds IS NOT NULL
            AND apgar_5min IS NOT NULL
    """

    # Run the query.
    client.https://docs.cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.client.Client.html#google_cloud_bigquery_client_Client_query_and_wait(query, job_config=job_config)  # Waits for the query to finish

## 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).