How to run a BigQuery query in Python

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This is the query that I have been running in BigQuery that I want to run in my python script. How would I change this/ what do I have to add for it to run in Python.

#standardSQL
SELECT
  Serial,
  MAX(createdAt) AS Latest_Use,
  SUM(ConnectionTime/3600) as Total_Hours,
  COUNT(DISTINCT DeviceID) AS Devices_Connected
FROM `dataworks-356fa.FirebaseArchive.testf`
WHERE Model = "BlueBox-pH"
GROUP BY Serial
ORDER BY Serial
LIMIT 1000;

From what I have been researching it is saying that I cant save this query as a permanent table using Python. Is that true? and if it is true is it possible to still export a temporary table?


You need to use the BigQuery Python client lib, then something like this should get you up and running:

from google.cloud import bigquery
client = bigquery.Client(project='PROJECT_ID')
query = "SELECT...."
dataset = client.dataset('dataset')
table = dataset.table(name='table')
job = client.run_async_query('my-job', query)
job.destination = table
job.write_disposition= 'WRITE_TRUNCATE'
job.begin()

https://googlecloudplatform.github.io/google-cloud-python/stable/bigquery-usage.html

See the current BigQuery Python client tutorial.

Downloading BigQuery data to pandas using the BigQuery Storage , You need to use the BigQuery Python client lib, then something like this should get you up and running: from google.cloud import bigquery  Clean up. In the Cloud Console, go to the Manage resources page. In the project list, select your project then click Delete . In the dialog, type the project ID and then click Shut down to delete the project.


This is a good usage guide: https://googleapis.github.io/google-cloud-python/latest/bigquery/usage/index.html

To simply run and write a query:

# from google.cloud import bigquery
# client = bigquery.Client()
# dataset_id = 'your_dataset_id'

job_config = bigquery.QueryJobConfig()
# Set the destination table
table_ref = client.dataset(dataset_id).table("your_table_id")
job_config.destination = table_ref
sql = """
    SELECT corpus
    FROM `bigquery-public-data.samples.shakespeare`
    GROUP BY corpus;
"""

# Start the query, passing in the extra configuration.
query_job = client.query(
    sql,
    # Location must match that of the dataset(s) referenced in the query
    # and of the destination table.
    location="US",
    job_config=job_config,
)  # API request - starts the query

query_job.result()  # Waits for the query to finish
print("Query results loaded to table {}".format(table_ref.path))

How to run a BigQuery query in Python, For more information please visit Python 2 support on Google Cloud. Running Queries¶. Querying data¶. Run a query and wait for it to finish with the query()  Essentially, we are running a query on a BigQuery table, running the Python method compute_fit, and writing the output to a BigQuery table. This is my compute_fit method. As you can see, it’s just


I personally prefer querying using pandas:

# BQ authentication
import pydata_google_auth
SCOPES = [
    'https://www.googleapis.com/auth/cloud-platform',
    'https://www.googleapis.com/auth/drive',
]

credentials = pydata_google_auth.get_user_credentials(
    SCOPES,
    # Set auth_local_webserver to True to have a slightly more convienient
    # authorization flow. Note, this doesn't work if you're running from a
    # notebook on a remote sever, such as over SSH or with Google Colab.
    auth_local_webserver=True,
)

query = "SELECT * FROM my_table"

data = pd.read_gbq(query, project_id = MY_PROJECT_ID, credentials=credentials, dialect = 'standard')

Running Queries, Querying massive datasets can be time consuming and expensive without the right hardware and infrastructure. Google BigQuery solves this problem by  Executing Queries with Python With the BigQuery client, we can execute raw queries on a dataset using the query method which actually inserts a query job into the BigQuery queue. These queries are executed asynchronously in the sense that there is no timeout specified and that the program will wait for the job to complete.


The pythonbq package is very simple to use and a great place to start. It uses python-gbq.

To get started you would need to generate a BQ json key for external app access. You can generate your key here.

Your code would look something like:

from pythonbq import pythonbq

myProject=pythonbq(
  bq_key_path='path/to/bq/key.json',
  project_id='myGoogleProjectID'
)
SQL_CODE="""
SELECT
  Serial,
  MAX(createdAt) AS Latest_Use,
  SUM(ConnectionTime/3600) as Total_Hours,
  COUNT(DISTINCT DeviceID) AS Devices_Connected
FROM `dataworks-356fa.FirebaseArchive.testf`
WHERE Model = "BlueBox-pH"
GROUP BY Serial
ORDER BY Serial
LIMIT 1000;
"""
output=myProject.query(sql=SQL_CODE)

Python Client for Google BigQuery, Executing Queries with Python. With the BigQuery client, we can execute raw queries on a dataset using the query method which actually  Creating an authorized view in BigQuery. Create an authorized view to share query results with particular users and groups without giving them access to the underlying tables. Downloading BigQuery data to pandas. Download data to the pandas library for Python by using the BigQuery Storage API. Visualizing BigQuery data using Google Data Studio


Access your Data in Google BigQuery with Python and R, run query from python on bigquery. run query from python on bigquery. # 1. Cloud Auth -. # gcloud auth application-default login. # 2. Python g clould bigquery  Enter a valid BigQuery SQL query in the New Query text area. Click the Show Options button. Select the Batch option in the Query Priority section. (Optional) For Processing Location, click Unspecified and choose your data's location. Click the Run query button. bq . Enter the bq query command and include your query text.


run query from python on bigquery · GitHub, Essentially, we are running a query on a BigQuery table, running the Python method compute_fit, and writing the output to a BigQuery table. This document describes how to run parameterized queries in BigQuery. Running parameterized queries. BigQuery supports query parameters to help prevent SQL injection when queries are constructed using user input.


How to run Python code on your BigQuery table, enable the BigQuery API; How to authenticate API requests; How to install the Python client library; How to query the works of Shakespeare  Sorry this is being so challenging to find info on. You're looking for what's called Service Accounts which are documented in our Authorizing Access to the BigQuery API using OAuth 2.0 guide. Here's an example, using the Python client library, though you'll want to look at the referenced documentation for info on acquiring the appropriate