Google Search Results in Python

This Python package is meant to scrape and parse Google, Google Scholar, Bing, Baidu, Yandex, Yahoo, Ebay results using SerpApi.

The following services are provided:

SerpApi provides a script builder to get you started quickly.

Installation

Python 3.7+

pip install google-search-results

Link to the python package page

Quick start

from serpapi.google_search_results import GoogleSearchResults
client = GoogleSearchResults({"q": "coffee", "location": "Austin,Texas"})
result = client.get_dict()

This example runs a search about "coffee" using your secret api key.

The SerpApi service (backend)

searches on Google using the client: q = "coffee"
parses the messy HTML responses
return a standardizes JSON response The GoogleSearchResults class
Format the request
Execute GET http request against SerpApi service
Parse JSON response into a dictionary Et voila..

Alternatively, you can search:

Bing using BingSearchResults class
Baidu using BaiduSearchResults class
Yahoo using YahooSearchResults class
Ebay using EbaySearchResults class
Yandex using YandexSearchResults class
GoogleScholar using GoogleScholarSearchResults class

See the playground to generate your code.

Summary

Google Search Results in Python

Google Search API capability

Source code.

params = {
  "q": "client",
  "google_domain": "Google Domain", 
  "location": "Location Requested", 
  "device": "desktop|mobile|tablet",
  "hl": "Google UI Language",
  "gl": "Google Country",
  "safe": "Safe Search Flag",
  "num": "Number of Results",
  "start": "Pagination Offset",
  "serp_api_key": "Your SERP API Key", 
  # To be match
  "tbm": "nws|isch|shop", 
  # To be search
  "tbs": "custom to be search criteria",
  # allow async request
  "async": "true|false",
  # output format
  "output": "json|html"
}

# define the search client
client = GoogleSearchResults(params)
# override an existing parameter
client.params_dict["location"] = "Portland"
# search format return as raw html
html_results = client.get_html()
# parse results
dict_results = client.get_dict()
json_results = client.get_json()

Link to the full documentation

see below for more hands on examples.

How to set SERP API key

You can get an API key here if you don't already have one: https://serpapi.com/users/sign_up

The SerpApi api_key can be set globally:

GoogleSearchResults.SERP_API_KEY = "Your Private Key"

The SerpApi api_key can be provided for each client:

query = GoogleSearchResults({"q": "coffee", "serp_api_key": "Your Private Key"})

Example by specification

We love true open source, continuous integration and Test Drive Development (TDD). We are using RSpec to test our infrastructure around the clock to achieve the best QoS (Quality Of Service).

The directory test/ includes specification/examples.

Set your api key.

export API_KEY="your secret key"

Run test

make test

Location API

from serpapi.google_search_results import GoogleSearchResults
client = GoogleSearchResults({})
location_list = client.get_location("Austin", 3)
print(location_list)

it prints the first 3 location matching Austin (Texas, Texas, Rochester)

[   {   'canonical_name': 'Austin,TX,Texas,United States',
        'country_code': 'US',
        'google_id': 200635,
        'google_parent_id': 21176,
        'gps': [-97.7430608, 30.267153],
        'id': '585069bdee19ad271e9bc072',
        'keys': ['austin', 'tx', 'texas', 'united', 'states'],
        'name': 'Austin, TX',
        'reach': 5560000,
        'target_type': 'DMA Region'},
        ...]

Search Archive API

The search result are stored in temporary cached. The previous search can be retrieve from the the cache for free.

from serp.google_search_results import GoogleSearchResults
client = GoogleSearchResults({"q": "Coffee", "location": "Austin,Texas"})
search_result = client.get_dictionary()
search_id = search_result.get("search_metadata").get("id")
print(search_id)

Now let retrieve the previous search from the archive.

archived_search_result = GoogleSearchResults({}).get_search_archive(search_id, 'json')
print(archived_search_result.get("search_metadata").get("id"))

it prints the search result from the archive.

Account API

from serpapi.google_search_results import GoogleSearchResults
client = GoogleSearchResults({})
account = client.get_account()

it prints your account information.

Search Bing

from serpapi.bing_search_results import BingSearchResults
client = BingSearchResults({"q": "Coffee", "location": "Austin,Texas"})
data = client.get_json()

this code prints baidu search results for coffee as JSON.

https://serpapi.com/bing-search-api

Search Baidu

from serpapi.baidu_search_results import BaiduSearchResults
client = BaiduSearchResults({"q": "Coffee"})
data = client.get_json()

this code prints baidu search results for coffee as JSON. https://serpapi.com/baidu-search-api

Search Yandex

from serpapi.yandex_search_results import YandexSearchResults
client = YandexSearchResults({"text": "Coffee"})
data = client.get_json()

this code prints yandex search results for coffee as JSON.

https://serpapi.com/yandex-search-api

Search Yahoo

from serpapi.yahoo_search_results import YahooSearchResults
client = YahooSearchResults({"p": "Coffee"})
data = client.get_json()

this code prints yahoo search results for coffee as JSON.

https://serpapi.com/yahoo-search-api

Search Ebay

from serpapi.ebay_search_results import EbaySearchResults
client = EbaySearchResults({"_nkw": "Coffee"})
data = client.get_json()

this code prints ebay search results for coffee as JSON.

https://serpapi.com/ebay-search-api

Search Google Scholar

from serpapi.google_scholar_search_results import GoogleScholarSearchResults
client = GoogleScholarSearchResults({"q": "Coffee"})
data = client.get_json()

this code prints Google Scholar search results.

Generic search with SerpApiClient

from serpapi.serp_api_client import SerpApiClient
query = {"q": "Coffee", "location": "Austin,Texas", "engine": "google"}
client = SerpApiClient(query)
data = client.get_json()

This class enables to interact with any search engine supported by SerpApi.com

Search Google Images

from serpapi.google_search_results import GoogleSearchResults
client = GoogleSearchResults({"q": "coffe", "tbm": "isch"})
for image_result in client.get_json()['images_results']:
    link = image_result["original"]
    try:
        print("link: " + link)
        # wget.download(link, '.')
    except:
        pass

this code prints all the images links, and download image if you un-comment the line with wget (linux/osx tool to download image).

This tutorial covers more ground on this topic. https://github.com/serpapi/showcase-serpapi-tensorflow-keras-image-training

Search Google News

from serpapi.google_search_results import GoogleSearchResults
client = GoogleSearchResults({
    "q": "coffe",   # search client
    "tbm": "nws",  # news
    "tbs": "qdr:d", # last 24h
    "num": 10
})
for offset in [0,1,2]:
    client.params_dict["start"] = offset * 10
    data = client.get_json()
    for news_result in data['news_results']:
        print(str(news_result['position'] + offset * 10) + " - " + news_result['title'])

this script prints the first 3 pages of the news title for the last 24h.

Search Google Shopping

from serpapi.google_search_results import GoogleSearchResults
client = GoogleSearchResults({
    "q": "coffe",   # search client
    "tbm": "shop",  # news
    "tbs": "p_ord:rv", # last 24h
    "num": 100
})
data = client.get_json()
for shopping_result in data['shopping_results']:
    print(shopping_result['position']) + " - " + shopping_result['title'])

this script prints all the shopping results order by review order.

Google Search By Location

With SerpApi, we can build Google search from anywhere in the world. This code is looking for the best coffee shop per city.

from serpapi.google_search_results import GoogleSearchResults
for city in ["new york", "paris", "berlin"]:
  location = GoogleSearchResults({}).get_location(city, 1)[0]["canonical_name"]
  client = GoogleSearchResults({
      "q": "best coffee shop",   # search client
      "location": location,
      "num": 1,
      "start": 0
  })
  data = client.get_json()
  top_result = data["organic_results"][0]["title"]

Batch Asynchronous Searches

We do offer two ways to boost your searches thanks to async parameter.

Blocking - async=false - it's more compute intensive because the client would need to hold many connections. (default)
Non-blocking - async=true - it's way to go for large amount of query submitted by batch (recommended)

# Python 3.6+
#

# Operating system
import os

# regular expression library
import re

# safe queue (named Queue in python2)
from queue import Queue

# Time utility
import time

# SerpApi client
from serpapi.google_search_results import GoogleSearchResults

# store searches
search_queue = Queue()
        
# SerpApi client
client = GoogleSearchResults({
    "location": "Austin,Texas",
    "async": True
})

# loop through a list of companies
for company in ['amd','nvidia','intel']:
  print("execute async search: q = " + company)
  client.params_dict["q"] = company
  search = client.get_dict()
  print("add search to the queue where id: " + search['search_metadata']['id'])
  # add search to the search_queue
  search_queue.put(search)

print("wait until all search statuses are cached or success")

# Create regular client
client = GoogleSearchResults({"async": True})
while not search_queue.empty():
  search = search_queue.get()
  search_id = search['search_metadata']['id']

  # retrieve search from the archive - blocker
  print(search_id + ": get search from archive")
  search_archived =  client.get_search_archive(search_id)
  print(search_id + ": status = " + search_archived['search_metadata']['status'])
  
  # check status
  if re.search('Cached|Success', search_archived['search_metadata']['status']):
    print(search_id + ": search done with q = " + search_archived['search_parameters']['q'])
  else:
    # requeue search_queue
    print(search_id + ": requeue search")
    search_queue.put(search)
    
    # wait 1s
    time.sleep(1)

# self.assertIsNotNone(results["local_results"][0]["title"])
print('all searches completed')

This code shows how to run searches asynchronously. The search parameters must have {async: True}. This indicates that the client shouldn't wait for the search to be completed. The current thread that executes the search is now non-blocking which allows to execute thousand of searches in seconds. The SerpApi backend will do the processing work. The actual search result is defer to a later call from the search archive using get_search_archive(search_id). In this example the non-blocking searches are persisted in a queue: search_queue. A loop through the search_queue allows to fetch individual search result. This process can be easily multithreaded to allow a large number of concurrent search requests. To keep thing simple, this example does only explore search result one at a time (single threaded).

See example.

Change log

2020-03-25 @ 1.8

add support for Yandex, Yahoo, Ebay
clean-up test 2019-11-10 @ 1.7.1
increase engine parameter priority over engine value set in the class 2019-09-12 @ 1.7
Change namespace "from lib." instead: "from serpapi.google_search_results import GoogleSearchResults"
Support for Bing and Baidu 2019-06-25 @ 1.6
New search engine supported: Baidu and Bing

Conclusion

SerpApi supports all the major search engines. Google has the more advance support with all the major services available: Images, News, Shopping and more.. To enable a type of search, the field tbm (to be matched) must be set to:

isch: Google Images API.
nws: Google News API.
shop: Google Shopping API.
any other Google service should work out of the box.
(no tbm parameter): regular Google client.

The field tbs allows to customize the search even more.

The full documentation is available here.

mdp0999 / google-search-results-python