"""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 = "{}.{}".format(client.project, dataset_id)
dataset = bigquery.Dataset(dataset_id_full)
# Create the new BigQuery dataset.
dataset = client.create_dataset(dataset)
# In the new BigQuery dataset, create a reference to a new table for
# storing the query results.
table_ref = dataset.table("regression_input")
# Configure the query job.
job_config = bigquery.QueryJobConfig()
# Set the destination table to the table reference created above.
job_config.destination = table_ref
# 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.
query_job = client.query(query, job_config=job_config)
query_job.result() # Waits for the query to finish
