Write to destination table

  """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 
  bigquery 
 
 # Create a new Google BigQuery client using Google Cloud Platform project 
 # defaults. 
 client 
 = 
  bigquery 
 
 . 
  Client 
 
 () 
 # 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 
 = 
  bigquery 
 
 . 
  Dataset 
 
 ( 
 dataset_id_full 
 ) 
 # Create the new BigQuery dataset. 
 dataset 
 = 
 client 
 . 
  create_dataset 
 
 ( 
 dataset 
 ) 
 # Configure the query job. 
 job_config 
 = 
  bigquery 
 
 . 
  QueryJobConfig 
 
 () 
 # 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 
 . 
  query_and_wait 
 
 ( 
 query 
 , 
 job_config 
 = 
 job_config 
 ) 
 # Waits for the query to finish