En este notebook utilizaremos la API de Google para búsquedas personalizadas sobre un sitio de interés, en este caso Pywombat.

Requerimientos

requests : Realizar peticiones API JSON de Google
pandas : Mostrar resultados en forma de Tabla
nbval : Realizar Test (py.test) sobre notebook

import json
import requests
import pandas as pd

API_KEY="AIzaSyD6MLBUV45-lqHIOlfQ7xuGlkEaYMB7_P4"
URL_API=f"https://www.googleapis.com/customsearch/v1?q=exercise&cx=000045984045285095660:7xaecdr9bat&key={API_KEY}"

results=dict()
columns=('htmlTitle', 'link', 'htmlSnippet')

def getResults():
    data=dict()
    try:
        response=requests.get(URL_API)
        data=response.json()
        if 'error' in data:
            raise Exception(f"{data['error']['message']}")
    except Exception as error:
        #print(f"ERROR {error} in API {URL_API}")
        print(f"ERROR in API {URL_API}")
    return data

def loadExercise():
    with open('pywombat.json', 'r') as f:
        data=json.load(f)
        return data['items']

data=getResults()    

for idx, item in enumerate(data.get('items',[]), 1):
    title=item['htmlTitle']
    link=item['link']
    snippet=item['htmlSnippet']
    results[idx]={'htmlTitle':title,
                  'link':link,
                  'htmlSnippet':snippet}

df=pd.DataFrame(data=results, index=columns).T
df

context="Pywombat Exercises"
assert ("context" in data and 
        context == data['context']['title']),"Error in Google Custom Search API"
print("Google Search API working!")