Google Search Results in Python

This Python package is meant to scrape and parse search results from Google, Bing, Baidu, Yandex, Yahoo, Home Depot, eBay and more, 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 import GoogleSearch
search = GoogleSearch({
    "q": "coffee", 
    "location": "Austin,Texas",
    "api_key": "<your secret api key>"
  })
result = search.get_dict()

This example runs a search for "coffee" using your secret API key.

The SerpApi service (backend)

Searches Google using the search: q = "coffee"
Parses the messy HTML responses
Returns a standardized JSON response
The GoogleSearch class
Formats the request
Executes a GET http request against SerpApi service
Parses the JSON response into a dictionary

Et voilà...

Alternatively, you can search:

Bing using BingSearch class
Baidu using BaiduSearch class
Yahoo using YahooSearch class
DuckDuckGo using DuckDuckGoSearch class
eBay using EbaySearch class
Yandex using YandexSearch class
HomeDepot using HomeDepotSearch class
GoogleScholar using GoogleScholarSearch class
Youtube using YoutubeSearch class
Walmart using WalmartSearch
Apple App Store using AppleAppStoreSearch class
Naver using NaverSearch class

See the playground to generate your code.

Summary

Google Search Results in Python

Google Search API capability

Source code.

params = {
  "q": "coffee",
  "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",
  "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 search
search = GoogleSearch(params)
# override an existing parameter
search.params_dict["location"] = "Portland"
# search format return as raw html
html_results = search.get_html()
# parse results
#  as python Dictionary
dict_results = search.get_dict()
#  as JSON using json package
json_results = search.get_json()
#  as dynamic Python object
object_result = search.get_object()

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:

GoogleSearch.SERP_API_KEY = "Your Private Key"

The SerpApi api_key can be provided for each search:

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

Example by specification

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

The directory test/ includes specification/examples.

Set your API key.

export API_KEY="your secret key"

Run test

make test

Location API

from serpapi import GoogleSearch
search = GoogleSearch({})
location_list = search.get_location("Austin", 3)
print(location_list)

This prints the first 3 locations 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 results are stored in a temporary cache.
The previous search can be retrieved from the cache for free.

from serpapi import GoogleSearch
search = GoogleSearch({"q": "Coffee", "location": "Austin,Texas"})
search_result = search.get_dictionary()
assert search_result.get("error") == None
search_id = search_result.get("search_metadata").get("id")
print(search_id)

Now let's retrieve the previous search from the archive.

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

This prints the search result from the archive.

Account API

from serpapi import GoogleSearch
search = GoogleSearch({})
account = search.get_account()

This prints your account information.

Search Bing

from serpapi import BingSearch
search = BingSearch({"q": "Coffee", "location": "Austin,Texas"})
data = search.get_dict()

This code prints Bing search results for coffee as a Dictionary.

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

Search Baidu

from serpapi import BaiduSearch
search = BaiduSearch({"q": "Coffee"})
data = search.get_dict()

This code prints Baidu search results for coffee as a Dictionary.
https://serpapi.com/baidu-search-api

Search Yandex

from serpapi import YandexSearch
search = YandexSearch({"text": "Coffee"})
data = search.get_dict()

This code prints Yandex search results for coffee as a Dictionary.

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

Search Yahoo

from serpapi import YahooSearch
search = YahooSearch({"p": "Coffee"})
data = search.get_dict()

This code prints Yahoo search results for coffee as a Dictionary.

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

Search eBay

from serpapi import EbaySearch
search = EbaySearch({"_nkw": "Coffee"})
data = search.get_dict()

This code prints eBay search results for coffee as a Dictionary.

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

Search Home Depot

from serpapi import HomeDepotSearch
search = HomeDepotSearch({"q": "chair"})
data = search.get_dict()

This code prints Home Depot search results for chair as Dictionary.

https://serpapi.com/home-depot-search-api

Search Youtube

from serpapi import HomeDepotSearch
search = YoutubeSearch({"q": "chair"})
data = search.get_dict()

This code prints Youtube search results for chair as Dictionary.

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

Search Google Scholar

from serpapi import GoogleScholarSearch
search = GoogleScholarSearch({"q": "Coffee"})
data = search.get_dict()

This code prints Google Scholar search results.

Search Walmart

from serpapi import WalmartSearch
search = WalmartSearch({"query": "chair"})
data = search.get_dict()

This code prints Walmart search results.

Search Youtube

from serpapi import YoutubeSearch
search = YoutubeSearch({"search_query": "chair"})
data = search.get_dict()

This code prints Youtube search results.

Search Apple App Store

from serpapi import AppleAppStoreSearch
search = AppleAppStoreSearch({"term": "Coffee"})
data = search.get_dict()

This code prints Apple App Store search results.

Search Naver

from serpapi import NaverSearch
search = NaverSearch({"query": "chair"})
data = search.get_dict()

This code prints Naver search results.

Generic search with SerpApiClient

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

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

Search Google Images

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

This code prints all the image links,
and downloads the images if you un-comment the line with wget (Linux/OS X tool to download files).

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

Search Google News

from serpapi import GoogleSearch
search = GoogleSearch({
    "q": "coffe",   # search search
    "tbm": "nws",  # news
    "tbs": "qdr:d", # last 24h
    "num": 10
})
for offset in [0,1,2]:
    search.params_dict["start"] = offset * 10
    data = search.get_dict()
    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 headlines for the last 24 hours.

Search Google Shopping

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

This script prints all the shopping results, ordered by review order.

Google Search By Location

With SerpApi, we can build a Google search from anywhere in the world.
This code looks for the best coffee shop for the given cities.

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

Batch Asynchronous Searches

We offer two ways to boost your searches thanks to theasync parameter.

Blocking - async=false - more compute intensive because the search needs to maintain many connections. (default)
Non-blocking - async=true - the way to go for large batches of queries (recommended)

# Operating system
import os

# regular expression library
import re

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

# Time utility
import time

# SerpApi search
from serpapi import GoogleSearch

# store searches
search_queue = Queue()

# SerpApi search
search = GoogleSearch({
    "location": "Austin,Texas",
    "async": True,
    "api_key": os.getenv("API_KEY")
})

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

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

# Create regular search
while not search_queue.empty():
    result = search_queue.get()
    search_id = result['search_metadata']['id']

    # retrieve search from the archive - blocker
    print(search_id + ": get search from archive")
    search_archived = search.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(result)

        # wait 1s
        time.sleep(1)

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 it to execute thousands of searches in seconds. The SerpApi backend will do the processing work.
The actual search result is deferred 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 it to fetch individual search results.
This process can easily be multithreaded to allow a large number of concurrent search requests.
To keep things simple, this example only explores search results one at a time (single threaded).

See example.

Python object as a result

The search results can be automatically wrapped in dynamically generated Python object.
This solution offers a more dynamic, fully Oriented Object Programming approach over the regular Dictionary / JSON data structure.

from serpapi import GoogleSearch
search = GoogleSearch({"q": "Coffee", "location": "Austin,Texas"})
r = search.get_object()
assert type(r.organic_results), list
assert r.organic_results[0].title
assert r.search_metadata.id
assert r.search_metadata.google_url
assert r.search_parameters.q, "Coffee"
assert r.search_parameters.engine, "google"

Pagination using iterator

Let's collect links across multiple search results pages.

# to get 2 pages
start = 0
end = 40
page_size = 10

# basic search parameters
parameter = {
  "q": "coca cola",
  "tbm": "nws",
  "api_key": os.getenv("API_KEY"),
  # optional pagination parameter
  #  the pagination method can take argument directly
  "start": start,
  "end": end,
  "num": page_size
}

# as proof of concept 
# urls collects
urls = []

# initialize a search
search = GoogleSearch(parameter)

# create a python generator using parameter
pages = search.pagination()
# or set custom parameter
pages = search.pagination(start, end, page_size)

# fetch one search result per iteration 
# using a basic python for loop 
# which invokes python iterator under the hood.
for page in pages:
  print(f"Current page: {page['serpapi_pagination']['current']}")
  for news_result in page["news_results"]:
    print(f"Title: {news_result['title']}\nLink: {news_result['link']}\n")
    urls.append(news_result['link'])
  
# check if the total number pages is as expected
# note: the exact number if variable depending on the search engine backend
if len(urls) == (end - start):
  print("all search results count match!")
if len(urls) == len(set(urls)):
  print("all search results are unique!")

Examples to fetch links with pagination: test file, online IDE

Error management

SerpApi keeps error management simple.

backend service error or search fail
client error

If it's a backend error, a simple error message is returned as string in the server response.

from serpapi import GoogleSearch
search = GoogleSearch({"q": "Coffee", "location": "Austin,Texas", "api_key": "<secret_key>"})
data = search.get_json()
assert data["error"] == None

In some cases, there are more details available in the data object.

If it's a client error, then a SerpApiClientException is raised.

Change log

2023-03-10 @ 2.4.2

Change long description to README.md

2021-12-22 @ 2.4.1

add more search engine
- youtube
- walmart
- apple_app_store
- naver
raise SerpApiClientException instead of raw string in order to follow Python guideline 3.5+
add more unit error tests for serp_api_client

2021-07-26 @ 2.4.0

add page size support using num parameter
add youtube search engine

2021-06-05 @ 2.3.0

add pagination support

2021-04-28 @ 2.2.0

add get_response method to provide raw requests.Response object

2021-04-04 @ 2.1.0

Add home depot search engine
get_object() returns dynamic Python object

2020-10-26 @ 2.0.0

Reduce class name to Search
Add get_raw_json

2020-06-30 @ 1.8.3

simplify import
improve package for python 3.5+
add support for python 3.5 and 3.6

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 import GoogleSearch"
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 search.

The field tbs allows to customize the search even more.

The full documentation is available here.

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google-search-results

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