How to Scrape Shopify Stores with Python

In this article, I’ll walk you through how to scrape Shopify stores using Python. Whether you’re a developer, marketer, or researcher, this guide will show you how to set up everything and start collecting data effortlessly. Let’s get started!

What is Shopify?

Shopify is an eCommerce platform that allows businesses to create online stores. It provides store owners various tools to manage inventory, process payments, and handle logistics. Each Shopify store is built on a template that focuses on providing an easy-to-use interface for store owners and customers.

One of Shopify’s key features is its use of JSON files to store product data. These files contain all the necessary information about the products on the store, including titles, descriptions, prices, images, and variants.

Why Scrape Shopify Stores?

Scraping Shopify stores can serve various purposes, such as:

• Product Research: Collecting product data, including pricing, availability, and features.
• Competitor Analysis: Monitoring competitor stores for pricing changes or new product launches.
• Data Collection: Gathering large datasets for research or analysis purposes.

The good news is that Shopify makes accessing product data in JSON format relatively easy, simplifying the scraping process. Instead of dealing with complex HTML parsing, you can directly access product data in a structured format.

🛡️ Use Bright Data Proxies for Reliable Shopify Scraping

When scraping multiple Shopify stores or handling large product catalogs, your IP may get rate-limited or blocked. To avoid this, consider using Bright Data’s Residential or Datacenter Proxies. They help you rotate IPs, bypass geo-restrictions, and maintain stable access — especially useful when scraping at scale or across regions.

Interested in other providers? Check out my list of the best proxy providers!

Note: I am not affiliated with any of these providers.

Setting Up the Environment

Before we start writing the scraping code, we need to set up our environment. The first step is to ensure that we have the necessary Python libraries installed.

Step 1: Install Python Requests

The requests library is the primary tool we will use to send HTTP requests to the Shopify store’s API endpoint and retrieve the JSON data. To install it, run the following command in your terminal:

pip install requests

Step 2: Install JSON Library (Optional)

Python comes with a built-in JSON library, so you don’t need to install it separately. This library allows you to parse and manipulate JSON data easily.

import json

Now that our environment is set up, we are ready to start writing the scraping script.

Scraping Data from Shopify

Understanding Shopify’s JSON Structure

Shopify stores provide product data through the /products.json endpoint. This JSON endpoint contains all the product details, including:

• Title: The name of the product.
• ID: A unique identifier for the product.
• Variants: Different variations of the product, such as size or color.
• Images: Product images.
• Options: Product options, such as size or color.

For example, a simple API response might look like this:

{
"products": [
{
"id": 123456789,
"title": "Product 1",
"variants": [
{
"id": 987654321,
"title": "Small",
"price": "19.99"
}
],
"images": [
{
"src": "https://example.com/image.jpg"
}
]
}
]
}

The Scraping Script

Let’s start with the basic function that will scrape product data from a Shopify store.

import requests
import json
def scrape_shopify(url):
"""Scrape product data from a Shopify store"""
json_url = f"{url}products.json" # Add the '/products.json' to the store's base URL
products = [] # This will store the scraped products
try:
response = requests.get(json_url)
response.raise_for_status() # Raise an error if the request fails
data = response.json() # Convert the response to JSON
for product in data["products"]: # Iterate through each product
product_info = {
"title": product["title"],
"id": product["id"],
"variants": product["variants"],
"images": product["images"],
"options": product["options"]
}
products.append(product_info) # Add product information to our list
except requests.RequestException as e:
print(f"Error: {e}")
except json.JSONDecodeError:
print("Error parsing the JSON response.")
return products

Explanation of the Code

• We start by defining the function scrape_shopify(), which takes the base URL of the Shopify store.
• We construct the JSON URL by appending /products.json to the base URL.
• We use the requests.get() method to send an HTTP request to the Shopify store.
• If the request is successful, the response is converted to a JSON format.
• We then iterate over each product in the JSON response, extracting key details such as the product title, ID, variants, images, and options.
• We store each product’s information in a list called products, which will eventually be returned by the function.

Saving the Data to a File

Once the data is scraped, we need to store it for later use. Since we are dealing with nested data (variants, images, etc.), saving it in a JSON rather than a CSV file is best.

Here’s how to write the scraped data to a file:

def save_to_json(data, filename):
"""Save the scraped data to a JSON file"""
try:
with open(filename, "w", encoding="utf-8") as f:
json.dump(data, f, indent=4)
print(f"Data saved to {filename}")
except Exception as e:
print(f"Error saving file: {e}")

Putting It All Together

Now, let’s bring everything together in the main block to run the scraper and save the data.

if __name__ == "__main__":
shop_url = "https://example-store.com/" # Replace with the target Shopify store URL
products = scrape_shopify(shop_url) # Scrape the store
save_to_json(products, "products.json") # Save the scraped data to a file

This script will scrape the Shopify store at the given URL, retrieve all product data, and save it to a file called products.json.

Advanced Techniques for Shopify Scraping

Handling Pagination

Many Shopify stores have a large number of products spread across multiple pages. To scrape all products, we need to handle pagination. Fortunately, Shopify supports pagination in its API, which allows us to scrape data from multiple pages.

To handle pagination, we simply add a page parameter to the URL. For example:

json_url = f"{url}products.json?page={page_number}"

We can modify our scraping function to scrape multiple pages:

def scrape_shopify(url, total_pages):
all_products = []
for page in range(1, total_pages   1):
json_url = f"{url}products.json?page={page}"
products = requests.get(json_url).json()["products"]
for product in products:
product_info = {
"title": product["title"],
"id": product["id"],
"variants": product["variants"],
"images": product["images"],
"options": product["options"]
}
all_products.append(product_info)
return all_products

Using Proxies

Sometimes, websites block requests from the same IP address if too many requests are sent quickly. To avoid this, you can use proxies. Here’s how you can integrate proxies into your requests:

proxies = {
"http": "http://username:[email protected]",
"https": "http://username:[email protected]"
}
response = requests.get(json_url, proxies=proxies)

Conclusion

Scraping Shopify stores with Python is simple, thanks to the Shopify JSON API. Adding/products.json to a store’s URL allows you to easily access structured product data without the need for complicated HTML parsing. With just a few lines of Python code, you can gather details like product variants, images, and prices, and store them for analysis. To scrape larger stores, you can use techniques like handling pagination and integrating proxies for smoother scraping. Always make sure to follow the store’s terms of service and scrape responsibly.