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#!/usr/bin/env python

# Copyright 2018 Google Inc. All Rights Reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

def run_natality_tutorial(override_values={}):

# [START bigquery_query_natality_tutorial]

"""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'

# [END bigquery_query_natality_tutorial]

# To facilitate testing, we replace values with alternatives

# provided by the testing harness.

dataset_id = override_values.get("dataset_id", dataset_id)

# [START bigquery_query_natality_tutorial]

dataset = bigquery.Dataset(client.dataset(dataset_id))

# 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

# [END bigquery_query_natality_tutorial]

if __name__ == '__main__':

run_natality_tutorial()