Develop Reference

Can you delete rows in BigQuery from Python script?

Hi is there a way for deleting rows in BigQuery from a Python Script? I tried looking in the documentation and finding an example on internet, but I could not find anything.
Something that looks like this.
table_id = "a.dataset.table" # Table ID for faulty_gla_entry
statement = """ DELETE FROM a.dataset.table where value = 2 """
client.delete(table_id, statement)

Like #SergeyGeron stated. https://googleapis.dev/python/bigquery/latest/usage/index.html#bigquery-basics
has nice stuff.
Wrote something like this.
from google.cloud import bigquery
client = bigquery.Client()
query = """DELETE FROM a.dataset.table WHERE value = 4"""
query_job = client.query(query)
print(query_job.result())

here you can see a code a documentation to execute a query with python
You can see this example code, with the “Delete” statement.
from google.cloud import bigquery
client = bigquery.Client()
dml_statement = (
"Delete from dataset.Inventory where ID=5"
)
query_job = client.query(dml_statement) # API request
query_job.result() # Waits for statement to finish
How to build queries in BigQuery

How to append query results using BigQuery Python API

I cannot find a way to append results of my query to a table in BigQuery that already exists and is partitioned by hour.
I have only found this solution: https://cloud.google.com/bigquery/docs/writing-results#writing_query_results.
job_config = bigquery.QueryJobConfig(destination=table_id)
sql = """SELECT * FROM table1 JOIN table2 ON table1.art_n=table2.artn"""
# Start the query, passing in the extra configuration.
query_job = client.query(sql, job_config=job_config) # Make an API request.
query_job.result() # Wait for the job to complete.
But providing a destination table to bigquery.QueryJobConfig overwrites it, and I did not find that bigquery.QueryJobConfig would have an option to specify if_exists or something. As far as I understand, I need to apply job.insert to query results, but I do not understand how.
I also did not find any good advice around, maybe someone can point me to it?
Just in case, my real query is huge and I load it from a separate JSON file.

When you create your job_config, you need to set the write_disposition to WRITE_APPEND:
[..]
job_config = bigquery.QueryJobConfig(
allow_large_results=True,
destination=table_id,
write_disposition='WRITE_APPEND'
)
[..]
See here.

You can add below lines to append data into existing table:
job_config.write_disposition = 'WRITE_APPEND'
Complete Code:
from google.cloud import bigquery
client = bigquery.Client()
job_config = bigquery.QueryJobConfig(destination="myproject.mydataset.target_table")
job_config.write_disposition = 'WRITE_APPEND'
sql = """SELECT * FROM table1 JOIN table2 ON table1.art_n=table2.artn"""
query_job = client.query(sql, job_config=job_config)
query_job.result()

The parameter that you were looking for is called write_disposition. You want to use WRITE_APPEND to append to a table.

How to get console view of parameterised BigQuery query?

I'm running parameterised BigQuery queries inside a Flask app exactly as described in Google's docs.
I'm seeing some unexpected results, so simply want to print the query to my terminal/console for debug purposes. When I do this on only see the query with the parameterised placeholders, not the values.
Does anyone know how to get a view of the query with the values being run?
For example:
query = "select * from dogs where breed = #dog_breed"
query_params = [
bigquery.ScalarQueryParameter("dog_breed", "STRING", "kokoni")
]
job_config = bigquery.QueryJobConfig()
job_config.query_parameters = query_params
print(query) # This will only print query as above, not with value 'kokoni'
query_job = client.query(
query,
job_config=job_config,
)

You could use the list_jobs method to retrieve the information from the Job class, like in the example below:
from google.cloud import bigquery
client = bigquery.Client()
# List the 3 most recent jobs in reverse chronological order.
# Omit the max_results parameter to list jobs from the past 6 months.
print("Last 3 jobs:")
for job in client.list_jobs(max_results=3): # API request(s)
print(job.query)
print(job.query_parameters)

BigQuery Python Client: Creating a Table from Query with a Table Description

I'm using the python client to create tables via SQL as explained in the docs (https://cloud.google.com/bigquery/docs/tables) like so:
# 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))
This works well except that the client function for creating a table via SQL query uses a job_config object, and job_config receives a table_ref, not a table object.
I found this doc for creating tables with description here: https://google-cloud-python.readthedocs.io/en/stable/bigquery/usage.html, But this is for tables NOT created from queries.
Any ideas on how to create a table from query while specifying a description for that table?

Since you want to do more than only save the SELECT result to a new table the best way for you is not use a destination table in your job_config variable rather use a CREATE command
So you need to do 2 things:
Remove the following 2 lines from your code
table_ref = client.dataset(dataset_id).table('your_table_id')
job_config.destination = table_ref
Replace your SQL with this
#standardSQL
CREATE TABLE dataset_id.your_table_id
PARTITION BY DATE(_PARTITIONTIME)
OPTIONS(
description = 'this table was created via agent #123'
) AS
SELECT corpus
FROM `bigquery-public-data.samples.shakespeare`
GROUP BY corpus;

How to run a BigQuery query in Python

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.

Here is another way using a JSON file for the service account:
>>> from google.cloud import bigquery
>>>
>>> CREDS = 'test_service_account.json'
>>> client = bigquery.Client.from_service_account_json(json_credentials_path=CREDS)
>>> job = client.query('select * from dataset1.mytable')
>>> for row in job.result():
... print(row)

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))

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')

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)