If your main goal is to scrape Google Shopping results, you need to know that it gathers information on product prices, deals, and the ranking of competitors. This type of analysis is common among marketers, e-commerce professionals, and web analysts for monitoring market trends and evaluating their performance relative to competitors.
Service offers a wealth of information on competitors’ activities along with the market’s product visibility. However, automated data collection will always be bound by the platform’s terms of service. Such violations could result in Google imposing some form of restrictions.
In this guide, you will understand balancing compliance with flexibility considerations when operating the Google Shopping scraper and security approaches.
Many issues need to be settled when selecting a Google Shopping data extraction scraper, which include the objectives of the project, the required amount of data, resource allocation, as well as the skill level of the information-collecting personnel.
Generally, all tools fall into three broad categories:
These are best suited for individuals with either a basic or advanced understanding of programming. It is very structured and offers scraping of information that is tailored to each user's specific needs. That said, practical applications still have certain requirements: setting up a development environment, installing required libraries and dependencies, and writing the code. Due to these factors, beginners will not be able to utilize this tool. Other programmers could benefit from these tools when they need to scrape Google Shopping results:
One of the most significant problems when you try to scrape Google Shopping results is fetching content that is rendered dynamically by JavaScript. It becomes visible after the information has been rendered. This means traditional scraping tools aren’t able to capture it. The tools listed above address this issue by waiting until the page is fully rendered to capture the required elements. Additionally, these libraries provide the opportunity to start the browser (Chromium, Firefox or WebKit) in headless mode, control pages as normal users, and use proxies to evade blocks.
The services mentioned earlier were tailored for developers. Cloud-based platforms are more efficient for end users who need a straightforward technique to extract data from Google Shopping.
Some of the most popular options include:
Using these cloud services is especially helpful because they add proxy support. This facilitates the removal of geographical limits, block evasion, and stable scraping. Such automated systems enable reliable extraction even at high volumes, thanks to automated IP rotation and CAPTCHA protection.
Google does not provide an open API meant for competitor research or catalog monitoring. The official Content API is meant solely for uploading and managing one's own products in the Merchant Center and not for retrieving information about other listings. For this reason, third-party APIs are frequently used for competitor analysis to gain unobstructed access to the required data.
APIs offer a structured layout of the product information, such as price, description, ratings, etc. This greatly assists in processing and reduces the chances of breaching terms of service, while allowing for greater automation.
Oxylabs Scraper API is an automated system for scraping from multiple sources such as Google Shopping. It employs sophisticated proxy handling, IP change, and anti-scraping techniques. You only need to send it an HTTP request with the relevant parameters, such as a search query or URL, and receive a JSON-formatted response containing all the data.
When compliance with set rules and regulations is a top priority for your project, SerpApi is a great option. It pulls out structured data without the need of manual HTML parsing. The tool fights back anti-bot measures, renders JavaScript, and provides clean info in JSON format.
To make use of the service, send a request with engine=google_shopping as a parameter together with the keyword you are searching for. SerpApi will go out to get the data and send it back in a desired format.
This tool automates scraping tasks to include changing IP addresses, evading blocks, managing sessions, and rendering changing content. It eliminates code writing and setting complex scraping parameters. All that is required is to forward an HTTP request with the target URL, and ScraperAPI will respond with a rendered HTML document.
Yes, you can scrape Google Shopping results using free datacenter proxies, but there are limits. Free proxies work for small projects or testing.
If you need to scrape Google Shopping frequently or gather data from specific locations, free proxies fall short.
At that scale, paid proxy plans are essential. Residential rotating proxies offer better success rates because they appear as real users from different IPs and locations. This reduces bans and CAPTCHAs when you scrape Google Shopping or scrape Google inline shopping results.
To get started with free proxies, providers like Oxylabs, Bright Data, and Smartproxy offer trial or sample proxies.
Before scraping, you need a custom script to handle requests. Your script must:
Tools like Postman help you test your HTTP requests, while browser developer tools allow you to inspect Google Shopping’s network traffic. These tools make it easier to build and debug your scripts.
To scrape Google Shopping results we'll start with using Python scripts with Selenium. This particular tool was chosen because it processes JavaScript dependent content.
If using Python 3, it's better to specify explicitly: pip3 install selenium.
To upgrade to the latest version of the library, use: pip install --upgrade selenium.
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Polski from selenium import webdriver
from webdriver_manager.chrome import ChromeDriverManager
from selenium.webdriver.chrome.service import Service
from selenium.webdriver.chrome.options import Options
# Proxy settings
PROXY = "IP:PORT" # Your IP and port
USERNAME = "username"
PASSWORD = "password"
chrome_options = Options()
chrome_options.add_argument(f'--proxy-server=http://{PROXY}')# Launch browser
service = Service(ChromeDriverManager().install())
driver = webdriver.Chrome(service=service, options=chrome_options)
# Navigate to Google
driver.get("https://google.com")# Navigate to Google Shopping
search_query = "phone" # Your search query
driver.get(f"https://www.google.com/search?q={search_query}&tbm=shop")
# Wait for elements to load
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
try:
# Wait for product cards to appear
products = WebDriverWait(driver, 10).until(
EC.presence_of_all_elements_located((By.CSS_SELECTOR,
"div.sh-dgr__grid-result"))
)
for product in products:
name = product.find_element(By.CSS_SELECTOR, "h3.tAxDx").text
price = product.find_element(By.CSS_SELECTOR, "span.a8Pemb").text
link = product.find_element(By.TAG_NAME, "a").get_attribute("href")
print(f"Product: {name}\nPrice: {price}\nLink: {link}\n")
except Exception as e:
print("Parsing error:", e)This script can be reused with any desired product keyword. If you are unsure of the trending products and require an initial list of keywords, we recommend reviewing the guide on how to scrape Google Trends.
You’ll learn how to scrape Google Shopping results cleanly and efficiently using Python and a powerful API.
First, make sure Python 3.6+ is installed. Run: pip install requests pandas. Verify the setup by importing these libraries in Python.
Next, prepare your API request payload. The core elements include:
Refer to the proxy-seller.com API docs for detailed parameter info and rate limits.
Now, send the HTTP POST request:
Once you get the JSON response, parse it for:
Loop through the “organic_results” array to build Python lists or dictionaries. Then convert these into Pandas DataFrames for better data handling.
Save your data locally as CSV or JSON files. Include timestamps in filenames for version control.
To scrape detailed product info, use product tokens to request richer data:
Extract pricing options by parsing nested JSON fields:
Create tables to allow price comparison across sellers. Save pricing data alongside product metadata.
For proxies, consider Proxy-Seller. They offer:
Using Proxy-Seller ensures smooth and scalable scraping of Google Shopping results with reliable proxies tailored for your needs.
When you scrape Google Shopping results, it is critical to both extract and organize the information in an appropriate manner. A dataset that is properly structured can be analyzed, filtered, stored and retrieved easily.
The platform permits the extraction of different kinds of information. This includes:
When dealing with data, it is best to work with a unique product identifier, if it exists. If not, it can be manually created.
Another important factor to consider is the date and time the data was captured, as this allows for tracking of price changes over periods of time. For datasets that will undergo regular updates, it is best to version the data by keeping each updated version written into a separate table.
Manual analysis and utilizing Business Intelligence (BI) tools allow for the data to be stored in Excel or CSV format. Should the data be required to be incorporated with other services and APIs or stored in NoSQL databases, integration with JSON becomes beneficial.
Automating or scheduling data collection is best suited for relational databases, such as MySQL, PostgreSQL, or SQLite. For fast integration and collaborative work, cloud-based software like Airtable, Google Sheets, or BigQuery offer an accessible solution.
Scraping Google Shopping is tricky. Google uses multiple methods to detect bots.
To deal with these challenges, consider using specialized scraping APIs or proxies with built-in anti-bot features. For example, Oxylabs offers a Google Shopping API that removes the technical headaches, letting you focus on data use.
Also, use monitoring tools that alert you about scraping failures or drops in data quality. This helps maintain consistent access to reliable Google Shopping data.
To sum up, if you make a decision to scrape Google Shopping results, it requires navigating legal restrictions while selecting the appropriate scraper to fulfill the task. Selenium, Playwright, Puppeteer, Apify, and SerpApi are the best for working with dynamically generated content, while static pages can be worked on using requests and BeautifulSoup.
It is critical earlier on in the process to identify which specific pieces of information to extract, as well as how to format them for subsequent analysis and storage. For persistent or periodic data retrieval, databases or cloud storage solutions are preferable to streamline task automation. Also, proxy servers are important, as they maintain the consistent and secure functionality of the scraper under frequent requests while also preventing blocks from the platform.
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