Skip to content

How to Scrape Google Shopping Results: A Comprehensive Guide

Google Shopping is an invaluable resource for consumers and businesses alike. It allows users to easily search, compare prices, and find the best deals on millions of products from thousands of online stores.

For businesses, having access to Google Shopping data can provide critical competitive insights on product listings, pricing trends, customer reviews, and more. This data can empower strategic decisions around pricing, inventory, advertising, and overall e-commerce strategy.

In this comprehensive guide, we‘ll walk through the step-by-step process for scraping various types of data from Google Shopping using Python.

Overview of Google Shopping

Google Shopping, formerly known as Froogle, is Google‘s price comparison shopping site and search engine. It was launched in 2002 as Froogle before being rebranded in 2012.

Google Shopping displays product listings from thousands of online stores that have partnered with Google through its Merchant Center. Users can search for products, compare prices across retailers, read reviews, and find deals.

For retailers, Google Shopping acts as an e-commerce platform to showcase their products and drive traffic to their online stores. Retailers bid on keywords so their products appear in search results when users search for those terms.

Before scraping any website, it‘s important to ensure you are legally allowed to scrape that data. Generally, publicly available data that does not require authentication can be scraped for research or analysis purposes. However, always check the target website‘s terms of service to confirm.

It‘s also good practice to throttle your requests, use proxies, and identify your scraper to avoid overloading the target servers. Make sure not to violate Google Shopping‘s terms of service by reselling scraped data.

Use the scraped data ethically. Do not use it for malicious purposes like undercutting competitors‘ prices in an anti-competitive manner.

Pages to Scrape on Google Shopping

There are three main types of pages on Google Shopping that contain valuable data:

Search Results Page

This page displays the products matching your search query, along with product titles, prices, merchant names, ratings, etc.

Product Page

This page shows details of a specific product like title, description, images, reviews, specs, and pricing info from different merchants.

Pricing Page

This page lists the prices offered by different merchants selling that product along with shipping costs, seller ratings, available offers, etc.

Let‘s look at how to scrape each of these pages in Python.

Scraping Google Shopping Using Python

We‘ll be using the Requests library to send requests to the website and Pandas to parse and store the scraped data in a structured format.

The following steps apply to scraping any of the Google Shopping page types:

1. Import Libraries

import pandas as pd
import requests

2. Create Payload

The payload contains the parameters for the specific Google Shopping page we want to scrape. This includes the page type, search query, number of pages, locale, filters, etc.

For example:

payload = {
  ‘source‘: ‘google_shopping_search‘,
  ‘query‘: ‘laptops‘, 
  ‘pages‘: 2

3. Send Request

We‘ll make a POST request to the Google Shopping API endpoint and pass our payload.

response =, 
                         auth=(‘username‘, ‘password‘),

4. Parse Response

The JSON response contains the structured data from the scraped page. We can parse and extract the relevant fields into a Pandas DataFrame.

data = response.json()

# Extract needed fields into dataframe
df = pd.DataFrame(columns=[‘Name‘, ‘Price‘, ‘Seller‘]) 

for item in data[‘results‘]:
  name = item[‘title‘]
  price = item[‘price‘]
  seller = item[‘merchant‘]

  df = df.append({‘Name‘: name, 
                  ‘Price‘: price, 
                  ‘Seller‘: seller}, 

5. Export Data

We can export the scraped DataFrame into a workable format like CSV or JSON.

df.to_csv(‘google_shopping.csv‘, index=False)
df.to_json(‘google_shopping.json‘, orient=‘records‘)

Now let‘s go through scraping each specific page type in more detail.

Scraping Google Shopping Search Results Page

To scrape the search results page, we need to pass the following parameters in the payload:

payload = {
  ‘source‘: ‘google_shopping_search‘,
  ‘domain‘: ‘com‘,  
  ‘query‘: ‘laptops‘,
  ‘pages‘: 2,

  # Additional filters
  ‘context‘: {
    ‘sort_by‘: ‘pd‘ # Price desc

The key data we can extract includes:

  • Product title
  • Product price
  • Seller name
  • Ratings
  • Shipping cost
  • Offers/deals

Here is some sample code to extract and store this data from the search results page:

# Extract data

title = data[‘results‘][0][‘title‘]
price = data[‘results‘][0][‘price‘]
seller = data[‘results‘][0][‘merchant‘][‘name‘]
rating = data[‘results‘][0][‘rating‘]

# Store in dataframe
df = df.append({‘Title‘: title, 
                ‘Price‘: price,
                ‘Seller‘: seller,
                ‘Rating‘: rating}, 

# Export to CSV
df.to_csv(‘search_results.csv‘, index=False)

Scraping Google Shopping Product Page

To scrape a product page, we pass the product ID in the payload:

product_id = ‘abcde12345‘ # Example product ID 

payload = {
  ‘source‘: ‘google_shopping_product‘,
  ‘domain‘: ‘com‘,
  ‘query‘: product_id 

For a product page, we can extract:

  • Product title
  • Description
  • Images
  • Specs
  • Ratings
  • Review count
  • Seller info

And sample code:

# Extract product data

title = data[‘results‘][0][‘content‘][‘title‘]
desc = data[‘results‘][0][‘content‘][‘description‘] 
image = data[‘results‘][0][‘content‘][‘images‘][0]
rating = data[‘results‘][0][‘content‘][‘rating‘]

# Store in dataframe 
df = df.append({‘Title‘: title,
                ‘Description‘: desc,
                ‘Image‘: image,
                ‘Rating‘: rating},

# Export to CSV
df.to_csv(‘product_page.csv‘, index=False)

Scraping Google Shopping Pricing Page

To get the pricing page for a product, we pass the product ID:

product_id = ‘abcde12345‘

payload = {
  ‘source‘: ‘google_shopping_pricing‘,
  ‘domain‘: ‘com‘,
  ‘query‘: product_id

For the pricing page, we can extract:

  • Product title
  • Seller names
  • Seller prices
  • Shipping charges
  • Total costs
  • Available offers

Sample code:

# Extract pricing data

title = data[‘results‘][0][‘content‘][‘title‘]

for seller in data[‘results‘][0][‘content‘][‘pricing‘]:
  name = seller[‘merchant‘][‘name‘]
  price = seller[‘price‘]
  shipping = seller[‘shipping‘]

  total = seller[‘total‘]
  offer = seller[‘offer‘]

  df = df.append({‘Title‘: title, 
                  ‘Seller‘: name,
                  ‘Price‘: price,
                  ‘Shipping‘: shipping,
                  ‘Total‘: total,
                  ‘Offer‘: offer}, ignore_index=True)

# Export to CSV  
df.to_csv(‘pricing_page.csv‘, index=False)

Comparison of Scraping Methods

There are a couple approaches for scraping Google Shopping:

  • Using Google Shopping API – Recommended method. More reliable, accurate, and fast. Can handle large data volumes.
  • Scraping directly in browser – More complex. Need to render JavaScript which is slower. Higher chance of getting blocked. Harder to scale.

The Google Shopping API provides a robust and scalable solution for scraping with proper headers, proxies, throttling built-in. For large or complex data needs, the API method is preferable.

Browser scraping may work for smaller one-off needs but requires more effort to avoid detection.

Challenges To Consider

There are some challenges to keep in mind when scraping Google Shopping:

  • Blocking and blacklisting – Google actively blocks scrapers. Need to use proxies and randomize headers/IP addresses.
  • Javascript rendering – Some data only loads dynamically via JavaScript. Scrapers need to be able to execute JS to scrape properly.
  • Large data volumes – Google Shopping has huge inventory with constant updates. Scrapers need to handle large data volumes and frequent changes.
  • Anti-scraping measures – Google employs advanced anti-bot measures like captchas and behavior analysis. Scrapers must mimic human behavior.
  • Data accuracy – With catalog-sized inventory, some product data can be incomplete or inaccurate. Important to confirm data quality.
  • Legal compliance – As mentioned earlier, ensure compliance with Google‘s terms of service and local laws.


I hope this guide provides a comprehensive overview of how to effectively scrape various types of pages on Google Shopping using Python and the Requests library. The key steps are:

  • Construct the payload with the required parameters
  • Make the request to the Google Shopping API
  • Parse and extract relevant data from the JSON response
  • Store the scraped data in a Pandas DataFrame
  • Export the DataFrame to CSV, JSON or another usable format

Scraping product search results, product pages, and pricing pages can give you valuable data on product catalog, pricing, competitive intelligence, and more. With the right approach, you can rapidly gather key insights from Google Shopping data at scale.

Let me know if you have any other questions! I‘m always happy to help fellow developers with web scraping projects.

Join the conversation

Your email address will not be published. Required fields are marked *