File size: 3,402 Bytes
1856027 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | #!/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.
from typing import Dict, Optional
def run_natality_tutorial(override_values: Optional[Dict[str, str]] = None) -> None:
if override_values is None:
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"
dataset_id_full = f"{client.project}.{dataset_id}"
# [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)
dataset_id_full = f"{client.project}.{dataset_id}"
# [START bigquery_query_natality_tutorial]
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
# [END bigquery_query_natality_tutorial]
if __name__ == "__main__":
run_natality_tutorial()
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