Using bigquery easily in Google Colab. Focus on a single table.

bq_easy.py

import re
import pandas as pd
from google.cloud import bigquery
from google.colab import auth

PROJECT_ID = 'sql-hunt'  # change to your own project

class BqTable:
  
  def __init__(self, table, dataset='samples', active_project='bigquery-public-data', client=None):
    
    if client:  # allow reuse client from other table
      c = client
    else:
      c = bigquery.Client(project=PROJECT_ID) # for connection only
    self.client = c
    
    self.project = active_project
    self.dataset = dataset
    self.table = table
    self.full_name = f"`{active_project}.{dataset}.{table}`"
    # get real table object
    self.d = c.get_dataset(c.dataset(dataset, active_project))
    self.t = c.get_table(self.d.table(table))
    self.schema = self.t.schema
    self.max_gb = 1.  # change it to set limit
    
  def query(self, query):
    query_size = self.query_size(query)
    if query_size <= self.max_gb:
      job = self.client.query(query)
      rows = list(job.result(timeout=30))
      if len(rows) == 0:
          print("Query returned no rows.")
          return None
      columns = list(rows[0]._xxx_field_to_index.keys())
      return pd.DataFrame(   
          data=[list(x.values()) for x in rows], columns=columns)
    print(f"Query cancelled; estimated size of {query_size} exceeds limit of {self.max_gb} GB")    
    
  def select(self, columns, where=None, group_by=None, order_by=None, limit=None):
    query = f"SELECT {columns} FROM {self.full_name}"
    if where:
      if not re.match('WHERE|GROUP|ORDER', where, re.I):
        where = 'WHERE ' + where
      query += " " + where
    if group_by:
      query += " GROUP BY " + group_by
    if order_by:
      query += " ORDER BY " + order_by
    if limit:
      query += " LIMIT " + str(limit)
    return self.query(query)

        
  def query_size(self, query):
    config = bigquery.QueryJobConfig()
    config.dry_run = True
    job = self.client.query(query, job_config=config)
    return job.total_bytes_processed / 2**30   # GB
  
  def head(self, num_rows=5, start_index=None, columns=None):
    if type(columns)==str:
      columns = re.split(', *', columns)
    schema = None
    if columns:
      schema = [col for col in self.t.schema if col.name in columns]
        
    results = self.client.list_rows(self.t, selected_fields=schema,
        max_results=num_rows, start_index=start_index)
    rows = [x for x in results]
    columns = list(rows[0]._xxx_field_to_index.keys())
    return pd.DataFrame(     
        data=[list(x.values()) for x in rows], columns=columns)
  
  @property
  def size(self):  
    query = f"""
      SELECT SUM(size_bytes) /POW(2,30)
      FROM `{self.project}.{self.dataset}.__TABLES__`
      WHERE table_id='{self.table}'
      """
    job = self.client.query(query)
    gb = list(job.result())[0][0]
    return gb
  
  def __str__(self):
    '''Use in query e.g. SELECT f FROM {tbl}'''
    return self.full_name
  
  def __repr__(self):
    params = f'"{self.table}", "{self.dataset}"'
    if self.project != 'bigquery-public-data':
      params += f', "self.project"'
    return f'BqTable({params})'
  
  def view(self, columns, where=None, group_by=None, order_by=None, limit=None):
    '''Same as select(), but for WITH-AS'''
    query = f"SELECT {columns} FROM {self.full_name}"
    if where:
      if not re.match('WHERE|GROUP|ORDER', where, re.I):
        where = 'WHERE ' + where
      query += " " + where
    if group_by:
      query += " GROUP BY " + group_by
    if order_by:
      query += " ORDER BY " + order_by
    if limit:
      query += " LIMIT " + str(limit)
    return '('+ query +')'

Usage:

from google.colab import auth
auth.authenticate_user()

aq = BqTable('global_air_quality', 'openaq')
print(aq.size)
aq.head()

Easy to select from the table

aq.select('location, latitude', 'latitude>70')

The result is shown as a pandas table.

Now add __str__() for easily reference to its full_name.
Also add .view() to use with WITH_AS statement.