Scraping eBay with ScrapeGraphAI

eBay is one of the world’s leading online marketplaces. By scraping eBay, you can easily monitor prices, analyze competitors, and gather market insights for your business. This guide shows you how to extract keyboard names and prices from eBay using ScrapeGraphAI with simple code examples in Python, JavaScript, and cURL.

Why Scrape eBay?

Scraping eBay is essential for businesses and analysts who want to leverage real-time e-commerce data to stay ahead of market trends. Here are the key reasons why extracting data from eBay is crucial:

• Price Monitoring: eBay hosts millions of products from various sellers. By scraping eBay, businesses can track price fluctuations, compare prices between different sellers, and optimize their own pricing strategies to remain competitive.
• Market Research: Understanding customer preferences is key to making data-driven decisions. Scraping eBay enables businesses to analyze trending products, best-selling items, and customer demand patterns.
• Competitive Analysis: With thousands of competing products listed on eBay, scraping allows businesses to analyze competitor offerings, pricing strategies, and customer reviews, gaining insights to refine their own product listings.
• Real-Time Insights: eCommerce data changes rapidly. By automating data extraction, companies can access up-to-date information on stock availability, seasonal trends, and emerging product categories.
• SEO and Product Optimization: Scraping product titles, descriptions, and customer reviews helps in understanding the best keywords for optimizing listings, improving search rankings, and increasing visibility on eBay and search engines like Google.
• Sales and Demand Forecasting: eBay data provides valuable insights into purchasing behavior, allowing businesses to predict demand and optimize inventory management.

How ScrapeGraphAI Simplifies eBay Scraping

ScrapeGraphAI automates data extraction and validates the output with a defined schema, ensuring you get structured data quickly and reliably.

Python Example

python
from scrapegraph_py import Client
from scrapegraph_py.logger import sgai_logger
from pydantic import BaseModel, Field

# Set logging level to INFO
sgai_logger.set_logging(level="INFO")

# Define the schema for the product listing
class ProductListingSchema(BaseModel):
    KeyboardName: str = Field(..., description="The name of the keyboard")
    Price: float = Field(..., description="The price of the keyboard")

# Initialize the client with your API key
sgai_client = Client(api_key="sgai-********************")

# Request to extract keyboard names and prices from eBay
response = sgai_client.smartscraper(
    website_url="https://www.ebay.it/sch/i.html?_nkw=keyboards&_sacat=0&_from=R40&_trksid=p4432023.m570.l1313",
    user_prompt="Extract all the KeyboardName and Price from the product listings",
    output_schema=ProductListingSchema
)

print(f"Request ID: {response['request_id']}")
print(f"Result: {response['result']}")

sgai_client.close()

JavaScript Example with Zod

javascript
import { Client } from 'scrapegraph-js';
import { z } from "zod";

// Define the schema using Zod
const productListingSchema = z.object({
  KeyboardName: z.string(),
  Price: z.number(),
});

type ProductListingSchema = z.infer<typeof productListingSchema>;

// Initialize the client
const sgai_client = new Client("sgai-********************");

try {
  const response = await sgai_client.smartscraper({
    websiteUrl: "https://www.ebay.it/sch/i.html?_nkw=keyboards&_sacat=0&_from=R40&_trksid=p4432023.m570.l1313",
    userPrompt: "Extract all the KeyboardName and Price from the product listings",
    outputSchema: productListingSchema
  });
  console.log('Request ID:', response.requestId);
  console.log('Result:', response.result);
} catch (error) {
  console.error(error);
} finally {
  sgai_client.close();
}

cURL Example

sh
curl -X 'POST'   'https://api.scrapegraphai.com/v1/smartscraper'   -H 'accept: application/json'   -H 'SGAI-APIKEY: sgai-********************'   -H 'Content-Type: application/json'   -d '{
  "website_url": "https://www.ebay.it/sch/i.html?_nkw=keyboards&_sacat=0&_from=R40&_trksid=p4432023.m570.l1313",
  "user_prompt": "Extract all the KeyboardName and Price from the product listings",
  "output_schema": {
    "type": "object",
    "title": "ProductListingSchema",
    "properties": {
      "KeyboardName": {
        "type": "string",
        "description": "The name of the keyboard"
      },
      "Price": {
        "type": "number",
        "description": "The price of the keyboard"
      }
    },
    "required": ["KeyboardName", "Price"]
  }
}'

Example Response

A sample JSON response might look like this:

json
{
  "KeyboardName": [
    "Logitech K380 Multi-Device Bluetooth Keyboard",
    "Dell KB212-B QuietKey Keyboard",
    "HP Wireless Keyboard 300",
    "Microsoft Sculpt Ergonomic Keyboard",
    "Razer BlackWidow Elite Mechanical Gaming Keyboard"
  ],
  "Price": [
    29.99,
    19.99,
    34.99,
    89.99,
    129.99
  ]
}

Conclusion

Scraping eBay with ScrapeGraphAI allows you to quickly gather structured product data for price monitoring, market research, and competitive analysis. Whether you choose Python, JavaScript, or cURL, this tool makes data extraction efficient and straightforward.

With the power of eBay data scraping, businesses can enhance their pricing strategies, optimize product listings, and stay ahead in the competitive eCommerce landscape. By leveraging real-time product and pricing data, companies can make smarter decisions, improve SEO rankings, and maximize their online sales potential.

Happy scraping, and may your insights drive your next breakthrough!