Google is undoubtedly the most popular search engine today, providing relevant results for billions of searches every day. With the wealth of public information available on Google, it‘s natural that people want to extract and analyze this data – which is exactly what web scraping enables.
In this comprehensive tutorial, we‘ll explore the ins and outs of scraping Google search results using Python. While Google doesn‘t make it easy, with the right techniques and tools you can successfully scrape SERPs without getting blocked.
Understanding Google SERPs
SERP stands for Search Engine Results Page – the page you see after entering a search query on Google. The organic blue links are no longer the only contents of a SERP. Today‘s results pages contain a number of additional elements:
- Featured snippets – Summaries of results shown in a box at the top
- Knowledge panels – Boxes with key facts about a search term
- Images/Videos – Galleries of media relevant to the query
- Local pack – Map and listings for local businesses
- Related searches – Other queries users searched for
- Ads – Paid search ads relevant to the keywords
Scraping the full SERP provides richer data compared to just scraping the top organic results.
Is Scraping Google Legal?
There is no definitive answer, but scraping publicly visible information from search engines is generally not illegal. Google‘s terms do not explicitly prohibit scraping as long as it is done in a reasonable manner. However, make sure to consult a lawyer about the specifics of your use case.
Key things to keep in mind – scrape only public pages, don‘t fake or spoof identities, employ reasonable crawl rate, and don‘t violate Google‘s guidelines. Avoid scraping illegal/copyrighted content.
Challenges in Scraping Google
Although scraping SERPs is not illegal per se, Google actively tries to detect and block scrapers to deliver the best user experience. Some key challenges faced:
CAPTCHAs – Google prompts CAPTCHA challenges to determine if the visitor is a human or a bot. Advanced tools use OCR and other techniques to solve them.
Bot Detection – Beyond CAPTCHAs, Google employs advanced techniques like mouse movement analysis to identify bots. Proxies and residential IPs help avoid this.
Blocking – Scrapers at scale often get blocked on the IP or account level. Again, proxies are useful to bypass blocks.
Data Organization – With many result types, SERP data can be unstructured. Scrapers need to clean and organize it.
Speed Limits – Making too many rapid requests can get your scraper flagged. Introduce delays between requests.
Scraping Approaches
There are a few different approaches to scrape Google programmatically:
Using the Requests library – A simple way to scrape SERPs in Python. Handles sessions and proxies but rendering Javascript is a challenge.
Headless Browsers like Selenium – Launches an actual Chrome browser to render Javascript. Captures rendered HTML. Resource heavy.
Custom Scraping Tools – Services like SerpApi structurally scrape results via proxies and feeds data via API. Easy to use.
Keyword-based API – Some tools like Zenserp provide a keyword-based API without requiring URLs. Quick and flexible.
We‘ll focus on using the Requests library for simplicity, but the principles are the same across methods.
Scraping with the Requests Library in Python
We‘ll first walk through a simple scraping script to fetch Google results for a query using the Requests module:
import requests
headers = {
"User-Agent": "Mozilla/5.0"
}
params = {
"q": "coffee"
}
r = requests.get("https://www.google.com/search", headers=headers, params=params)
print(r.status_code)
print(r.text[:500])Here we set the User-Agent header to mimic a browser and pass our search term ‘coffee‘ as a URL parameter. Requests handles cookies, sessions etc. for us. We can print the HTML of the results page.
While this script works, it has some issues – results are likely blocked since we have no proxies. Also, the raw HTML is not structured data. Let‘s improve it.
Using Proxies for Scraping
To avoid getting blocked while scraping Google at scale, using proxies is crucial. Here are some ways to use proxies with Requests:
Rotate Proxy IPs – Pass a list of IPs to the ‘proxies‘ parameter in Requests. Keeps switching IPs.
Proxy Rotation Services – Services like Brighdata provide API access to thousands of proxies.
Residential Proxies – More expensive but residential IPs avoid detection better.
Here‘s an example using a list of IP:port proxies:
proxy_list = [
{"ip": "191.102.134.36", "port": 8888},
{"ip": "185.173.35.53", "port": 22225}
]
random_proxy = random.choice(proxy_list)
proxies = {
"http": "http://{ip}:{port}".format(ip=random_proxy["ip"], port=random_proxy["port"]),
"https": "https://{ip}:{port}".format(ip=random_proxy["ip"], port=random_proxy["port"]),
}
requests.get("https://google.com", proxies=proxies)We randomly select a proxy from our list and format it into the proxies dict for Requests.
Parsing Results into Structured Data
While the raw HTML gives us access to data, it is unstructured and messy. To extract and analyze SERP data, we need:
- Organized JSON/CSV output
- Separate organic results from other elements like ads, maps etc.
We can use Python libraries like Beautiful Soup to parse HTML into JSON. For example:
from bs4 import BeautifulSoup
import json
# Scrape HTML
soup = BeautifulSoup(r.text, ‘html.parser‘)
results = []
for div in soup.select(‘.g‘):
title = div.select_one(‘.LC20lb.DKV0Md‘).text
link = div.select_one(‘.yuRUbf a‘)[‘href‘]
result = {
‘title‘: title,
‘link‘: link
}
results.append(result)
print(json.dumps(results, indent=2))Here we use CSS selectors to extract key parts from result divs. The final output is a JSON list containing title and link for each result.
Best Practices for Scraping Google
Here are some additional tips to scrape Google effectively:
- Use random delays between requests to mimic human behavior
- Rotate User Agents using a user agent list to avoid detection
- Handle errors like connectivity issues, captchas etc. gracefully
- Use a proxy service instead of own proxy servers to simplify management
- Scrape different locales by changing the Google domain to google.co.uk, google.es etc.
- Limit number of concurrent requests to avoid being flagged as DoS attack
- Consult Google‘s guidelines and stay updated on their policies
- Seek legal advice if scraping commercially or at large scale
Scraping Google Verticals
The techniques discussed can also be used to scrape other Google properties like Images, News, Books etc.
For example, to scrape Google Image results for ‘cats‘, make a request to the imgurl parameter:
https://www.google.com/search?tbm=isch&q=catsFor news results, use the tnorewrite parameter:
https://www.google.com/search?tbm=nws&q=cryptoGoogle Books, Patents, Finance and other verticals can be scraped in a similar fashion.
Conclusion
In this post we looked at how to effectively scrape Google search results using Python, considering both opportunities and challenges. With a well-designed scraper employing proxies and clean data parsing, you can extract SERP data without getting blocked.
As always when scraping public sites, be sure to comply with terms of service and employ sensible scraping practices. Used appropriately, scraping can unlock the power of Google‘s data at scale.
