Commands to connect to Big query -python

from google.cloud import bigquery

#create client object

client=bigquery.client()

#create reference

dataset_ref=client.dataset(“hacker_news”,project=”bigquery-public-data”)

#api request to fetch the dataset

dataset=client.get_dataset(dataset_ref)

#list all tables using list_tables() function

tables=list(client.list_tables(dataset))

For tables in tables:

               Print(table.table_id)

#create reference to full table

table_ref=dataset_ref.table(“full”)

#api request to fetch table

Table=client.get_table(table_ref)

Table.schema

output :- name, field type, mode(nullable), description

#preview first five rows of the “full” table

client.list_rows(table,max_result=5).to_dataframe()

# Preview the first five entries in the "by" column of the "full" table

client.list_rows(table, selected_fields=table.schema[:1], max_results=5).to_dataframe()

Triple quotes are used to preserve the format

#create  queryjobconfig object to estimate size

dry_run_config = bigquery.QueryJobConfig(dry_run=True)

#API request – dry run query to estimate costs

dry_run_query_job = client.query(query, job_config=dry_run_config)

print(“This query will process {} bytes.” .format(dry_run_query_job.total_bytes_processed)

#limiting query to 1 mb

one_MB = 1000*1000

safe_config=client.query(query,job_config=safe_config)

safe_query_job.to_dataframe()