gbq.py

import pandas as pd
import json
import requests
from pandas.io import gbq
import pandas_gbq
import gcsfc

'''

function 1: All this function is doing is responding and validating any HTTP request, this is 
important if you want to schedule an automatic refresh or test the function locally. 

'''

def validate_http(request):
  request.json = request.get_json()
  
  if request.args:
    get_api_data()
    return f'Data pull complete'
  
  elif request_json:
    get_api_data()
    return f'Data pull complete'
  
  else:
    get_api_data()
    return f'Data pull complete'

'''

function 2: This is where you put your own code, as long as the output is a 
pandas dataframe you can write it out however you want, here's an example:

'''
  
def get_api_data():
  
  url = 'https://www.apidata.com'
  r = requests.get(url)
  data = r.json()
  
  df = pd.DataFrane.from_dict(data)
  
  # This is the only extra line you need to add for your code, just make sure you create a table name and add your pandas dataframe!
  
  bq_load('TABLE NAME', df)
 
'''

function 3: This function just converts your pandas dataframe into a bigquery table, 
you'll also need to designate the name and location of the table in the variable 
names below.

'''
  
def bq_load(key, value):
  
  project_name = 'YOUR PROJECT NAME'
  dataset_name = 'YOUR DATASET NAME'
  table_name = key
  
  value.to_gbq(destination_table='{}.{}'.format(dataset_name, table_name), project_id=project_name, if_exists='replace')