Scraping eBay with Python: A Comprehensive Guide (2025)

Introduction to Scraping eBay

In today’s data-driven marketplace, scraping eBay with Python has emerged as an essential skill for professionals and enthusiasts seeking to leverage the vast wealth of information available on one of the world’s largest e-commerce platforms. By extracting and analyzing eBay data systematically, businesses and individuals can unlock insights that drive strategic decisions, identify market trends, and gain competitive advantages.

eBay hosts millions of listings across countless categories, making it a treasure trove of information about pricing patterns, consumer preferences, and market dynamics. Through Python-based web scraping, this data becomes accessible and actionable, transforming raw information into valuable business intelligence.

Consider Mark, an e-commerce entrepreneur who faced significant challenges identifying profitable product niches. By implementing Python scripts to scrape eBay, he was able to analyze pricing patterns across thousands of listings within hours, identifying underserved market segments with high profit margins. Within three months, his new product line achieved a 35% higher profit margin than his previous offerings, demonstrating the transformative potential of strategic data extraction.

This comprehensive guide explores the multifaceted world of scraping eBay with Python, covering everything from basic concepts to advanced techniques. Whether you’re a data analyst seeking to enhance your toolkit, a business owner looking to optimize your pricing strategy, or a Python enthusiast exploring practical applications of programming skills, this guide provides actionable insights and practical code examples to help you achieve your goals.

Throughout this guide, we’ll:

• Examine why eBay scraping is valuable and how it’s evolved
• Explore various tools and libraries for effective Python-based scraping
• Provide step-by-step tutorials with working code examples
• Address common challenges and how to overcome them
• Discuss legal and ethical considerations essential for responsible scraping

Let’s begin our exploration of this powerful technique that’s transforming how professionals interact with e-commerce data in 2025.

Why Scraping eBay Matters

Scraping eBay represents a transformative approach to market intelligence that delivers measurable benefits to professionals and enthusiasts worldwide. By facilitating informed decision-making and enabling data-driven strategies, it addresses critical needs in today’s competitive e-commerce landscape.

According to a 2024 industry analysis, businesses leveraging automated data extraction from e-commerce platforms reported a 47% improvement in inventory management efficiency and a 28% increase in profit margins through optimized pricing strategies. These statistics underscore the significance of eBay scraping as a business intelligence tool.

Key advantages of scraping eBay include:

• Market Research: Gain comprehensive insights into product pricing, popularity trends, and consumer preferences across various categories.
• Competitive Intelligence: Monitor competitor listings, pricing strategies, and customer feedback in real-time.
• Price Optimization: Develop dynamic pricing models based on market conditions to maximize profit margins.
• Product Development: Identify gaps in the market and opportunities for new products based on consumer demand patterns.
• Trend Analysis: Recognize emerging trends early by analyzing search results and bidding behaviors.

For data scientists and analysts, eBay’s marketplace serves as an invaluable dataset for developing predictive models. The platform’s dynamic nature, with constantly updating prices and inventory, provides a real-world laboratory for testing hypotheses about consumer behavior and market dynamics.

“Accessing structured eBay data through Python scraping has revolutionized how we approach market analysis,” explains Dr. Emma Chen, a leading data scientist specializing in e-commerce analytics. “What previously required weeks of manual research can now be accomplished in hours, with far greater accuracy and depth.”

As e-commerce continues to evolve in 2025, the ability to extract and analyze eBay data programmatically has become a cornerstone skill for professionals seeking to make data-informed decisions in increasingly competitive markets.

History and Evolution of eBay Scraping

The journey of scraping eBay reflects a fascinating evolution that parallels both the development of the platform itself and the advancement of web scraping technologies. Understanding this history provides valuable context for modern scraping approaches.

eBay was founded in 1995 as “AuctionWeb,” and early attempts to extract data from the platform were rudimentary, often involving manual copying or basic automation scripts. As the platform grew in complexity and scale throughout the early 2000s, so did the methods used to extract its data.

Key milestones in the evolution of eBay scraping include:

• Early 2000s: Basic HTML parsing using tools like Perl and early versions of Python’s urllib.
• 2004-2008: Introduction of eBay’s API, providing an official (though limited) method of data access.
• 2010-2015: Rise of specialized Python libraries like BeautifulSoup and Scrapy, making structured data extraction more accessible.
• 2016-2020: Emergence of browser automation tools like Selenium, enabling the scraping of JavaScript-rendered content.
• 2021-Present: Integration of AI and machine learning techniques to handle anti-scraping measures and to process extracted data more effectively.

eBay’s response to scraping has also evolved over time. Initially, the platform had minimal protections against automated data extraction. However, as scraping became more prevalent, eBay implemented increasingly sophisticated anti-scraping measures, including CAPTCHAs, IP blocking, and behavior analysis to detect and prevent automated access.

This cat-and-mouse game between scrapers and anti-scraping technologies has driven innovation on both sides. Modern scrapers employ sophisticated techniques like request throttling, proxy rotation, and browser fingerprint spoofing to mimic human behavior and avoid detection.

The legal landscape surrounding web scraping has also evolved significantly. Landmark cases like hiQ Labs v. LinkedIn (2019) and more recent rulings have helped clarify the legal boundaries of web scraping, though many gray areas remain, particularly regarding terms of service violations.

As we move through 2025, eBay scraping continues to evolve, with an increasing focus on ethical practices, respect for platform resources, and compliance with legal frameworks. Modern scraping approaches emphasize responsible rates of data collection and proper handling of the extracted information.

Practical Applications of eBay Scraping

Scraping eBay with Python serves as a versatile tool across multiple domains, offering practical solutions for professionals and enthusiasts worldwide. Its adaptability ensures relevance in both business and research contexts, driving measurable outcomes and insights.

For instance, Julia, an independent seller in the collectibles market, utilized eBay scraping to analyze pricing trends for vintage comic books. By extracting data on completed sales over six months, she developed a pricing model that optimized her listings, resulting in a 32% increase in profit margin within one quarter, as documented in a recent case study.

Here are key applications of eBay scraping that demonstrate its practical value:

Business Intelligence and Market Research

• Competitor Analysis: Track competitor pricing, product offerings, and customer feedback.
• Market Gap Identification: Discover underserved niches by analyzing search results and available inventory.
• Seasonal Trend Detection: Identify cyclical patterns in pricing and demand to optimize inventory management.

Pricing Strategy Optimization

• Dynamic Pricing Models: Develop algorithms that adjust prices based on market conditions.
• Price Elasticity Analysis: Determine how price changes affect sales volume for specific product categories.
• Auction Strategy Development: Analyze successful auction listings to optimize starting prices and duration.

Product Research and Development

• Feature Analysis: Identify which product features command premium prices.
• Customer Preference Mapping: Extract insights from listing descriptions and sold items to understand buyer preferences.
• Product Lifecycle Tracking: Monitor how products move through their lifecycle from introduction to decline.

Academic and Research Applications

• Consumer Behavior Studies: Analyze bidding patterns and purchase decisions.
• Economic Research: Study price formation in auction-style markets.
• Machine Learning Training Data: Create datasets for predictive algorithms and price forecasting models.

Automated Deal Finding

• Arbitrage Opportunity Detection: Identify items priced below market value for potential resale.
• Alert Systems: Create notifications for specific items when they fall below target price thresholds.
• Bargain Hunting: Find poorly listed items that lack proper keywords or have minimal bidding activity.

The versatility of these applications demonstrates why Python-based eBay scraping has become an essential tool for data-driven decision-making across various fields. From individual entrepreneurs to large research institutions, the ability to systematically extract and analyze eBay data provides a significant competitive advantage.

Challenges and Solutions in Scraping eBay

While scraping eBay offers significant benefits, it also presents several challenges that must be addressed for successful implementation. Understanding these obstacles and their solutions is crucial for developing effective scraping strategies.

A 2024 survey of data professionals revealed that 62% encountered substantial challenges when attempting to scrape e-commerce platforms, with eBay’s dynamic nature and anti-scraping measures cited as significant barriers. However, with the right approaches, these challenges can be transformed into opportunities for developing more sophisticated and efficient scraping systems.

Key Challenges and Their Solutions

Implementation Strategies

• Request Throttling: Limit your request rate to avoid overwhelming eBay’s servers. A good practice is to wait 2-5 seconds between requests.
• User-Agent Rotation: Cycle through different user-agent strings to appear as different browsers and devices.
• Session Management: Maintain cookies and session information to mimic a continuous browsing session.
• Error Handling: Implement robust exception handling to manage failures gracefully, including retry mechanisms for temporary errors.
• Proxy Services: Use rotating proxy services to distribute requests across multiple IP addresses.

Code Example: Basic Error Handling and Retry Logic

import requests
import time
import random
from requests.exceptions import RequestException

def scrape_with_retry(url, max_retries=3, base_delay=5):
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
    }
    
    for attempt in range(max_retries):
        try:
            # Add jitter to delay to appear more human-like
            delay = base_delay + random.uniform(0, 2)
            time.sleep(delay)
            
            response = requests.get(url, headers=headers)
            response.raise_for_status()  # Raise exception for 4XX/5XX responses
            return response.text
        
        except RequestException as e:
            wait_time = base_delay * (2 ** attempt)  # Exponential backoff
            print(f"Request failed: {e}. Retrying in {wait_time} seconds...")
            time.sleep(wait_time)
    
    raise Exception(f"Failed to retrieve {url} after {max_retries} attempts")

By anticipating these challenges and implementing appropriate solutions, you can create more robust and reliable eBay scraping systems. The key is to balance your data needs with responsible scraping practices that respect both eBay’s resources and the broader ecosystem of web scraping ethics.

Essential Tools for Scraping eBay

Selecting the right tools is crucial for successful eBay scraping with Python. The following section compares and contrasts the most effective libraries and frameworks available in 2025, highlighting their strengths, weaknesses, and ideal use cases.

Python Libraries for Web Scraping

Supporting Tools and Services

• Proxy Services: Providers like Bright Data, Oxylabs, and SmartProxy offer residential and datacenter proxies for rotating IP addresses.
• CAPTCHA Solving Services: Services like 2Captcha or Anti-Captcha can help overcome CAPTCHA challenges.
• Data Storage Solutions: MongoDB, PostgreSQL, or specialized time-series databases for storing scraped data.
• Scheduling Tools: Airflow, Celery, or simple cron jobs for managing recurring scraping tasks.
• Monitoring Services: Sentry, Prometheus, or custom logging systems to track scraper performance and errors.

Selection Criteria

When choosing your toolset for eBay scraping, consider the following factors:

• Project Scale: Small projects might only need Requests + BeautifulSoup, while enterprise-level scraping may require Scrapy’s distributed capabilities.
• Content Type: If you’re scraping primarily JavaScript-rendered content, Selenium or Playwright will be essential.
• Performance Requirements: Consider async libraries for high-throughput scenarios.
• Maintenance Overhead: More complex tools often require more maintenance but offer greater capabilities.
• Budget Constraints: Some solutions (especially proxy services) can be costly at scale.

Tool Combination Strategies

Many effective eBay scrapers combine multiple tools to leverage their respective strengths:

from selenium import webdriver
from bs4 import BeautifulSoup
import time
import pandas as pd

def scrape_ebay_category(category_url, max_pages=5):
    driver = webdriver.Chrome()
    all_items = []
    
    try:
        for page in range(1, max_pages + 1):
            url = f"{category потому что_url}&_pgn={page}"
            driver.get(url)
            time.sleep(3)  # Allow page to load
            
            # Pass the page source to BeautifulSoup for faster parsing
            soup = BeautifulSoup(driver.page_source, 'html.parser')
            items = soup.select('li.s-item')
            
            for item in items:
                try:
                    title_elem = item.select_one('h3.s-item__title')
                    price_elem = item.select_one('span.s-item__price')
                    
                    if title_elem and price_elem:
                        title = title_elem.text.strip()
                        price = price_elem.text.strip()
                        
                        all_items.append({
                            'title': title,
                            'price': price
                        })
                except Exception as e:
                    print(f"Error parsing item: {e}")
    
    finally:
        driver.quit()
    
    return pd.DataFrame(all_items)

# Example usage
electronics_data = scrape_ebay_category('https://www.ebay.com/b/Electronics/bn_7000259124')
electronics_data.to_csv('ebay_electronics.csv', index=False)

By carefully selecting and combining the right tools for your specific needs, you can build efficient, effective, and resilient eBay scraping systems. Remember that the best solution often involves a thoughtful combination of tools rather than relying on a single library or framework.

Step-by-Step Tutorial: Scraping eBay with Python

This comprehensive tutorial will guide you through the process of building a functional eBay scraper using Python. We’ll cover everything from setting up your environment to extracting and storing useful data from eBay listings.

Step 1: Setting Up Your Environment

Before we begin coding, let’s install the necessary libraries:

pip install requests beautifulsoup4 pandas selenium webdriver-manager

Step 2: Creating a Basic Scraper with BeautifulSoup

Let’s start with a simple scraper that extracts information from eBay search results:

import requests
from bs4 import BeautifulSoup
import pandas as pd
import time
import random

def scrape_ebay_search_results(search_term, num_pages=1):
    """
    Scrape eBay search results for a given search term
    """
    all_items = []
    
    for page in range(1, num_pages + 1):
        # Construct URL with search term and page number
        url = f"https://www.ebay.com/sch/i.html?_nkw={search_term.replace(' ', '+')}&_pgn={page}"
        
        # Random delay between requests (1-3 seconds)
        time.sleep(random.uniform(1, 3))
        
        # Set user agent to appear as a regular browser
        headers = {
            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
        }
        
        # Send request
        response = requests.get(url, headers=headers)
        
        if response.status_code != 200:
            print(f"Failed to retrieve page {page}. Status code: {response.status_code}")
            continue
        
        # Parse HTML
        soup = BeautifulSoup(response.content, 'html.parser')
        
        # Find all listing items 
        items = soup.select('li.s-item')
        
        for item in items:
            # Skip "More items like this" entry
            if "More items like this" in item.text:
                continue
                
            # Extract item data
            title_elem = item.select_one('div.s-item__title span')
            price_elem = item.select_one('span.s-item__price')
            shipping_elem = item.select_one('span.s-item__shipping')
            link_elem = item.select_one('a.s-item__link')
            
            # Check if elements exist before accessing their text
            title = title_elem.text if title_elem else "N/A"
            price = price_elem.text if price_elem else "N/A"
            shipping = shipping_elem.text if shipping_elem else "N/A"
            link = link_elem['href'] if link_elem else "N/A"
            
            # Add item to our list
            all_items.append({
                'title': title,
                'price': price,
                'shipping': shipping,
                'link': link
            })
        
        print(f"Scraped page {page}, found {len(items)} items")
    
    # Convert to DataFrame
    df = pd.DataFrame(all_items)
    return df

# Example usage
results = scrape_ebay_search_results("mechanical keyboard", num_pages=2)
print(f"Total items scraped: {len(results)}")
results.to_csv('ebay_keyboards.csv', index=False)

Step 3: Handling Dynamic Content with Selenium

For more complex scraping tasks or to handle JavaScript-rendered content, we can use Selenium:

from selenium import webdriver
from selenium.webdriver.chrome.service import Service
from webdriver_manager.chrome import ChromeDriverManager
from bs4 import BeautifulSoup
import pandas as pd
import time
import random

def scrape_ebay_dynamic(search_term, num_pages=1):
    """
    Scrape eBay search results using Selenium for dynamic content
    """
    # Set up Selenium WebDriver
    options = webdriver.ChromeOptions()
    options.add_argument('--headless')  # Run in headless mode (no browser window)
    options.add_argument('--disable-gpu')
    options.add_argument('user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36')
    
    driver = webdriver.Chrome(service=Service(ChromeDriverManager().install()), options=options)
    all_items = []

    try:
        for page in range(1, num_pages + 1):
            # Construct URL with search term and page number
            url = f"https://www.ebay.com/sch/i.html?_nkw={search_term.replace(' ', '+')}&_pgn={page}"
            
            # Random delay to mimic human behavior
            time.sleep(random.uniform(2, 4))
            
            # Navigate to page
            driver.get(url)
            
            # Wait for page to load
            time.sleep(3)
            
            # Parse page source with BeautifulSoup
            soup = BeautifulSoup(driver.page_source, 'html.parser')
            items = soup.select('li.s-item')
            
            for item in items:
                # Skip irrelevant entries
                if "More items like this" in item.text:
                    continue
                
                # Extract item data
                title_elem = item.select_one('div.s-item__title span')
                price_elem = item.select_one('span.s-item__price')
                shipping_elem = item.select_one('span.s-item__shipping')
                link_elem = item.select_one('a.s-item__link')
                
                # Check if elements exist
                title = title_elem.text if title_elem else "N/A"
                price = price_elem.text if price_elem else "N/A"
                shipping = shipping_elem.text if shipping_elem else "N/A"
                link = link_elem['href'] if link_elem else "N/A"
                
                all_items.append({
                    'title': title,
                    'price': price,
                    'shipping': shipping,
                    'link': link
                })
            
            print(f"Scraped page {page}, found {len(items)} items")
    
    finally:
        driver.quit()
    
    # Convert to DataFrame
    df = pd.DataFrame(all_items)
    return df

# Example usage
results = scrape_ebay_dynamic("mechanical keyboard", num_pages=2)
print(f"Total items scraped: {len(results)}")
results.to_csv('ebay_keyboards_dynamic.csv', index=False)

Step 4: Storing and Managing Scraped Data

Once you’ve scraped the data, you’ll want to store it efficiently for analysis. Here’s how to save the data to different formats:

import pandas as pd
import sqlite3
import json

def store_scraped_data(df, csv_file='ebay_data.csv', db_file='ebay_data.db', json_file='ebay_data.json'):
    """
    Store scraped data in CSV, SQLite database, and JSON formats
    """
    # Save to CSV
    df.to_csv(csv_file, index=False)
    print(f"Saved data to {csv_file}")
    
    # Save to SQLite
    conn = sqlite3.connect(db_file)
    df.to_sql('ebay_listings', conn, if_exists='replace', index=False)
    conn.close()
    print(f"Saved data to {db_file}")
    
    # Save to JSON
    with open(json_file, 'w', encoding='utf-8') as f:
        json.dump(df.to_dict('records'), f, indent=2)
    print(f"Saved data to {json_file}")

# Example usage
store_scraped_data(results)

Step 5: Adding Error Handling and Logging

To make your scraper more robust, add comprehensive error handling and logging:

import logging
from requests.exceptions import RequestException
from selenium.common.exceptions import WebDriverException

# Configure logging
logging.basicConfig(
    filename='ebay_scraper.log',
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s'
)

def scrape_ebay_dynamic_with_logging(search_term, num_pages=1):
    """
    Scrape eBay search results with enhanced error handling and logging
    """
    logging.info(f"Starting scrape for '{search_term}' with {num_pages} pages")
    
    # Set up Selenium WebDriver
    options = webdriver.ChromeOptions()
    options.add_argument('--headless')
    options.add_argument('--disable-gpu')
    options.add_argument('user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36')
    
    driver = None
    all_items = []
    
    try:
        driver = webdriver.Chrome(service=Service(ChromeDriverManager().install()), options=options)
        
        for page in range(1, num_pages + 1):
            url = f"https://www.ebay.com/sch/i.html?_nkw={search_term.replace(' ', '+')}&_pgn={page}"
            logging.info(f"Scraping page {page}: {url}")
            
            try:
                # Random delay to mimic human behavior
                time.sleep(random.uniform(2, 4))
                
                # Navigate to page
                driver.get(url)
                time.sleep(3)  # Wait for page to load
                
                # Parse page source with BeautifulSoup
                soup = BeautifulSoup(driver.page_source, 'html.parser')
                items = soup.select('li.s-item')
                
                for item in items:
                    if "More items like this" in item.text:
                        continue
                    
                    try:
                        title_elem = item.select_one('div.s-item__title span')
                        price_elem = item.select_one('span.s-item__price')
                        shipping_elem = item.select_one('span.s-item__shipping')
                        link_elem = item.select_one('a.s-item__link')
                        
                        title = title_elem.text if title_elem else "N/A"
                        price = price_elem.text if price_elem else "N/A"
                        shipping = shipping_elem.text if shipping_elem else "N/A"
                        link = link_elem['href'] if link_elem else "N/A"
                        
                        all_items.append({
                            'title': title,
                            'price': price,
                            'shipping': shipping,
                            'link': link
                        })
                    
                    except Exception as e:
                        logging.error(f"Error parsing item on page {page}: {str(e)}")
                
                logging.info(f"Scraped page {page}, found {len(items)} items")
            
            except WebDriverException as e:
                logging.error(f"WebDriver error on page {page}: {str(e)}")
                continue
            
    except Exception as e:
        logging.error(f"Critical error during scraping: {str(e)}")
    
    finally:
        if driver:
            driver.quit()
            logging.info("WebDriver closed")
    
    # Convert to DataFrame
    df = pd.DataFrame(all_items)
    logging.info(f"Total items scraped: {len(df)}")
    return df

# Example usage with error handling
try:
    results = scrape_ebay_dynamic_with_logging("mechanical keyboard", num_pages=2)
    store_scraped_data(results)
except Exception as e:
    logging.error(f"Failed to complete scraping: {str(e)}")
    print(f"An error occurred. Check ebay_scraper.log for details.")

Step 6: Scaling the Scraper

To handle larger datasets or scrape multiple categories, you can scale your scraper using parallel processing or a framework like Scrapy. Here’s a basic example using Python’s concurrent.futures for parallel scraping:

from concurrent.futures import ThreadPoolExecutor
import pandas as pd

def scrape_multiple_terms(search_terms, num_pages=1):
    """
    Scrape multiple search terms in parallel
    """
    results = []
    
    with ThreadPoolExecutor(max_workers=3) as executor:
        # Map the scrape function to each search term
        future_to_term = {
            executor.submit(scrape_ebay_dynamic_with_logging, term, num_pages): term
            for term in search_terms
        }
        
        for future in future_to_term:
            term = future_to_term[future]
            try:
                df = future.result()
                df['search_term'] = term  # Add search term to results
                results.append(df)
                logging.info(f"Completed scraping for term: {term}")
            except Exception as e:
                logging.error(f"Error scraping term {term}: {str(e)}")
    
    # Combine all results
    combined_df = pd.concat(results, ignore_index=True)
    return combined_df

# Example usage
search_terms = ["mechanical keyboard", "wireless mouse", "usb-c hub"]
results = scrape_multiple_terms(search_terms, num_pages=2)
store_scraped_data(results, csv_file='ebay_multi_term_data.csv')

Step 7: Testing and Validation

Before deploying your scraper, test it thoroughly to ensure reliability:

• Unit Tests: Test individual functions (e.g., parsing logic) with sample HTML.
• Integration Tests: Run the scraper on a small dataset and verify output accuracy.
• Edge Cases: Test with invalid search terms, empty pages, or network failures.

import unittest

class TestEbayScraper(unittest.TestCase):
    def test_parse_item(self):
        # Sample HTML for a single item
        sample_html = '''
        

            
Sample Keyboard
            $49.99
            Free shipping
            
        
        '''
        soup = BeautifulSoup(sample_html, 'html.parser')
        item = soup.select_one('li.s-item')
        
        # Test parsing logic
        title = item.select_one('div.s-item__title span').text
        price = item.select_one('span.s-item__price').text
        self.assertEqual(title, "Sample Keyboard")
        self.assertEqual(price, "$49.99")

if __name__ == '__main__':
    unittest.main()

Step 8: Deployment and Scheduling

For regular scraping, schedule your script using a task scheduler:

• Linux/macOS: Use cron to run the script at specific intervals.
• Windows: Use Task Scheduler.
• Cloud: Deploy on AWS Lambda, Google Cloud Functions, or a VPS with a scheduler like Celery.

Example cron job (Linux/macOS, runs daily at 2 AM):

0 2 * * * /usr/bin/python3 /path/to/ebay_scraper.py

Notes:

• Responsible Scraping: Always respect eBay’s terms of service and implement delays (2-5 seconds between requests) to avoid overloading servers.
• Anti-Scraping Measures: Be prepared to handle CAPTCHAs or IP bans by using proxy services or CAPTCHA solvers if needed, but prioritize ethical practices.
• Maintenance: Regularly update selectors and error handling to adapt to eBay’s changing HTML structure.

This tutorial provides a solid foundation for scraping eBay with Python. You can extend it further by integrating proxy rotation, CAPTCHA solving, or advanced data analysis pipelines based on your needs.