The 15 Most Common Web Scraping Errors and How to Fix Them

Web Scraping HTTP Status Code Errors

When you’re scraping the web, HTTP status codes are like the traffic signals of the internet—they let you know how the server responded to your request. Knowing what these codes mean can help you adjust your approach, troubleshoot errors, and keep your scraper running smoothly.

Here’s a quick breakdown:

• 1xx Informational: The server received your request and is processing it.
• 2xx Success: Your request was received and processed successfully—but don’t assume that means you got the data you wanted! Always check the response content.
• 3xx Redirection: The server is redirecting you to another page. Make sure your scraper follows these redirects to grab the correct content.
• 4xx Client Errors: Something went wrong on your end—either the request is invalid, or you don’t have the right permissions.
• 5xx Server Errors: The server ran into an issue processing your request. These are usually temporary problems.

Now, let’s explore the most common errors you’ll likely run into while scraping and, more importantly, how to solve them.

Client-Side Errors (4xx)

These errors typically occur when the problem lies with the request you’re sending. Here’s what each error means and how to resolve it:

401 Unauthorized

The server rejected your request due to missing or invalid credentials. It could be:

• Incorrect login credentials
• An expired session or authentication token
• Geographic restrictions limiting access

Solution:

• Double-check your login details and update any expired tokens
• Set up a system to refresh tokens automatically
• Bypass geographic restrictions using a proxy or VPN

403 Forbidden

The server understood your request but is refusing to fulfill it. This usually happens when:

• Your IP has been blocked
• You’re sending too many requests too quickly
• Your user-agent looks suspiciously like a scraper bot

Solution:

• Rotate your IP addresses using proxies to avoid detection
• Slow down your request rate to avoid triggering anti-scraping measures
• Change your user-agent string to mimic a regular web browser

404 Not Found

The page you’re trying to access doesn’t exist. This is often due to:

• A typo in the URL
• The page has been moved or deleted

Solution:

• Double-check the URL for errors
• Use tools like sitemaps or search engines to find the page’s new location

429 Too Many Requests

You’ve been sending too many requests in a short period of time, and the server is rate-limiting you.

Solution:

• Introduce delays between your requests using sleep timers
• Spread your requests across multiple IP addresses by using a proxy pool
• Check the site’s API documentation for rate limit guidelines and adjust accordingly

Server-Side Errors (5xx)

Server-side errors indicate problems on the server itself, often temporary, like overload or maintenance. Here’s how to handle them:

500 Internal Server Error

The server encountered an unexpected issue while processing your request. This could be due to:

• Server overload
• Misconfigurations or software errors on the server

Solution:

• Retry the request after a short delay – most 500 errors are temporary
• Implement an exponential backoff strategy, where each retry waits slightly longer to avoid hammering the server

502 Bad Gateway & 504 Gateway Timeout

These errors indicate communication issues between servers. A 502 error means one server received an invalid response from another, while 504 means the upstream server took too long to respond.

Solution:

• Check your proxy settings – misconfigured proxies can sometimes cause these errors
• Retry the request with a longer delay between attempts – in many cases, the issue will resolve on its own

503 Service Unavailable

The server is temporarily overloaded or down for maintenance.

Solution:

• Schedule your scraping tasks during off-peak hours to avoid high-traffic times
• Set up automatic retries to continue sending the request until the server is back online

Implementing Basic Error Handling

When scraping websites, it’s crucial to handle various HTTP errors gracefully. The following Python snippet shows how to use the requests library to make HTTP requests while catching and handling different types of errors. This basic error-handling approach allows your scraper to log issues and continue running even when it encounters problems:

<pre class="wp-block-syntaxhighlighter-code">
  import requests
  from requests.exceptions import RequestException
  
  def make_request(url):
      try:
          response = requests.get(url)
          response.raise_for_status()  # Raise HTTPError for bad responses (4xx, 5xx)
          return response.text
      except requests.HTTPError as http_err:
          print(f'HTTP error occurred: {http_err}')  # Handle HTTP-specific errors
      except RequestException as err:
          print(f'Other error occurred: {err}')  # Handle other potential errors
      return None
  
  url = 'https://example.com'
  content = make_request(url)
  if content:
      print('Request successful!')
  else:
      print('Failed to retrieve content')

You can easily integrate this function into your scraping scripts to add a layer of robustness to your web requests.

How ScraperAPI Can Simplify Web Scraping

ScraperAPI helps solve many of these web scraping challenges automatically. Here’s how:

• Automatic IP Rotation: ScraperAPI rotates IPs with each request to avoid IP bans or rate limits.
• User-Agent Management: It automatically chooses the right headers and generates cookies to mimic an actual user visiting the target site.
• Error Handling & Retries: ScraperAPI retries failed requests with built-in exponential backoff, saving you from manual error management.
• Geo-Location Proxies: Easily switch between proxies from different geographic locations to bypass region-specific restrictions.
• Request Throttling: Control the rate of your requests to avoid hitting rate limits.

With ScraperAPI handling the heavy lifting, you can focus on extracting the data you need without getting bogged down by common scraping errors.To get started, create a free ScraperAPI account and send your requests through its endpoint:

  import requests


  payload = {
     'api_key': 'YOUR_API_KEY',
     'url': 'https://books.toscrape.com/',
     'country_code': 'us'
  }
  
  
  response = requests.get('https://api.scraperapi.com', params=payload)
  print(response.status_code)

Explore all our solutions for more specific use cases.

Platform-Based Error Codes

While HTTP status codes give you a basic sense of what’s wrong with your request, platforms like Amazon, Etsy, and other ecommerce sites often throw unique error messages or use sophisticated anti-scraping mechanisms that go beyond standard responses. To successfully scrape these sites, you'll need to understand the errors you encounter and how to adjust your approach for each platform.

Amazon Scraping Errors

Amazon is notorious for its aggressive anti-scraping techniques, and instead of giving you clear error codes, it often responds with vague messages like:

“Sorry, something went wrong”

This error, although not an HTTP status code, indicates that Amazon’s anti-scraping measures have been triggered. It’s usually the result of aggressive request patterns, IP restrictions, or bot-detection systems. Here’s what typically causes it and how you can overcome these hurdles:

Common Reasons for Amazon's "Sorry, something went wrong" Error

1. IP Blocks or Rate Limits:
   • Just as with traditional HTTP rate-limiting errors, Amazon monitors request frequency closely. Too many requests from the same IP within a short time frame will get your IP blocked or temporarily restricted.
2. Bot Detection Mechanisms:
   • Amazon employs sophisticated methods to detect bots. Requests that repeat headers or user-agent strings or that arrive too quickly are likely to be flagged as automated, prompting this error.
3. Session or Cookie Issues:
   • When scraping behind login pages, invalid or expired sessions can also lead to this error. Amazon expects valid credentials and session cookies to maintain access; failing to refresh them can get you locked out.
4. JavaScript Rendering:
   • Amazon’s pages rely heavily on JavaScript to load product details, reviews, and other key data. If your scraper isn’t rendering JavaScript, you may see this error when trying to access dynamic content.

How to Handle Amazon's "Sorry, something went wrong" Error

Here’s how you can deal with the “something went wrong” error effectively:

1. Rotate IPs Regularly:

As mentioned earlier, IP rotation is key to avoiding blocks. This becomes even more important for Amazon due to its strict monitoring of IP activity. Use a proxy pool to rotate your IPs frequently, and favor residential or mobile proxies for better results.

Why: Constantly changing IPs helps spread out your requests and prevents any single IP from making too many requests in a short period—one of the main triggers for this error.

2. Throttle and Randomize Your Requests:

As we’ve seen with HTTP rate limits, slowing down your requests can help you stay under the radar. For Amazon, add random delays between requests, ranging from a few seconds to longer intervals, to mimic natural browsing.

Why: Sending requests too quickly is a dead giveaway for scraping activity. Randomizing the timing of your requests makes your scraper appear more human-like, reducing the chances of getting blocked.

3. Simulate Human Behavior:

Rotating user-agent strings and modifying headers, as discussed previously, is essential here. On Amazon, it’s especially important to simulate human browsing patterns by altering cookies and session data with each request.

Why: Amazon’s detection systems look for repetitive header and user-agent patterns. You can avoid triggering this error by randomizing these elements and ensuring valid session management.

4. Use a Headless Browser for JavaScript Rendering:

When scraping dynamic content on Amazon, switching to a headless browser like Puppeteer or Selenium is a smart move. These tools can render JavaScript, allowing your scraper to access dynamic elements that won’t load with a standard HTTP request.

Another great strategy is using ScraperAPI to handle JS rendering. This will allow you to scrape any dynamic page without the additional complexity of using a headless browser.

Why: Amazon relies heavily on JavaScript for core content, such as product data and customer reviews. Without properly rendering the page, your scraper may miss critical information and trigger errors.

As we discussed with rate-limiting errors, avoiding repetitive request patterns is important. Amazon tracks this behavior closely, so:

• Randomize interaction timing: Adding variability to your interactions, such as random delays between page loads or actions, makes your requests look more natural.
• Scrape during off-peak hours: Amazon may tighten restrictions during high-traffic periods. Running your scraper during less busy times can reduce the likelihood of encountering this error.

Simplifying Amazon Scraping with ScraperAPI

Tackling Amazon’s sophisticated anti-scraping measures can be complex, but tools like ScraperAPI’s Amazon endpoint offer a straightforward solution. By automating IP rotation, handling dynamic content, and managing request pacing, ScraperAPI takes care of the heavy lifting, allowing you to focus on the data.

With the Amazon-specific endpoint, you don’t need to manually implement workarounds for IP blocks, rate limits, or JavaScript-rendered content. Instead, it delivers a streamlined, ready-to-use solution tailored to scraping Amazon at scale without the hassle of constant adjustments.

For a deeper dive into how to scrape Amazon’s product data efficiently, check out this guide on scraping Amazon ASINs at scale. It walks you through specific strategies for working with Amazon data, including how ScraperAPI can help you gather product information effortlessly.

Etsy Scraping Errors

Etsy, like Amazon, employs aggressive anti-scraping mechanisms to protect its data. A common error faced by scrapers on Etsy is:

Etsy 429 Error – Too Many Requests

This error indicates that you’ve exceeded Etsy’s rate limit for requests in a short time frame. Etsy uses this method to prevent bots from overloading their servers with rapid-fire requests. It’s a clear sign that your scraping activity has been detected and temporarily blocked.

Common Reasons for Etsy's 429 Error

1. Excessive Request Frequency:
   • Etsy imposes rate limits on the number of requests that can be made from the same IP within a specific time period. If you exceed this limit and receive the 429 error, you'll be temporarily blocked.
2. Lack of IP Rotation:
   • Etsy can easily track the volume of requests from a single source without rotating IP addresses. Sending multiple requests from the same IP makes it more likely that Etsy’s rate-limiting system will kick in.
3. Bot-Like Patterns:
   • Etsy monitors browsing patterns and can detect bots based on fixed intervals between requests or identical user-agent strings. Repetitive or predictable behaviors will likely result in a block.

How to Handle Etsy's 429 Error

Here’s how you can avoid hitting Etsy’s rate limits and bypass the 429 error:

1. Throttle and Randomize Your Requests:

As with Amazon’s rate-limiting issues, slowing down and randomizing your requests can help avoid detection. Introduce varied delays between requests, ensuring that they mimic natural browsing behavior.

Why: Sending too many requests in a short time frame is the primary cause of the 429 error. Randomizing your intervals and reducing request frequency will help prevent Etsy from blocking your scraper.

2. Rotate IPs Regularly:

Use proxy pools to rotate your IP addresses frequently, as we’ve discussed in previous sections. For Etsy, rotating residential or mobile IPs will make your requests look more like those of regular users.

Why: Etsy can track the number of requests coming from each IP. By rotating IPs, you distribute your traffic across multiple addresses, preventing any one IP from exceeding the rate limit.

3. Simulate Human-Like Behavior:

Rotate user-agent strings and headers, as we’ve touched on before, to avoid repetitive patterns. Additionally, vary your interactions with the site, such as simulating clicks, scrolling, or pausing as a human user would.

Why: Etsy detects automation by tracking repetitive behaviors. Rotating headers, simulating natural user activity, and adjusting cookies or sessions can make your scraping more discreet.

4. Monitor and Adhere to Rate Limits:

Check Etsy’s API documentation to understand their specific rate limits. Stay within those limits, even when scraping HTML directly, to avoid hitting the 429 error.

Why: Rate limits are built into Etsy’s systems to protect the platform from overload. By respecting these thresholds, you reduce the risk of being blocked.

Etsy may impose stricter rate limits during peak traffic periods. To avoid getting blocked, consider running your scraping activities during off-peak hours when Etsy's defenses might be more relaxed. Randomizing interaction timing and avoiding predictable request patterns can help you stay under the radar.

Simplifying Etsy Scraping with ScraperAPI

Dealing with Etsy’s rate limits can be tricky, but ScraperAPI helps streamline the process. Its automatic IP rotation and request management make it easier to avoid 429 errors while scaling your data collection.

Walmart Scraping Errors

Walmart 429 Error – Too Many Requests

The 429 error occurs when Walmart detects that you are sending too many requests in a short period of time. This is a rate-limiting defense mechanism designed to prevent bots from overwhelming the server. It’s a signal that your scraper’s request frequency has been flagged, and Walmart has temporarily blocked further access.

Common Reasons for Walmart's 429 Error

1. Exceeding Rate Limits:
   • Walmart enforces strict rate limits, meaning that too many requests sent from the same IP in a short time frame will trigger the 429 error.
2. Lack of IP Rotation:
   • If you’re scraping without rotating IP addresses, Walmart can easily track the request volume from a single IP and block it once the rate limit is exceeded.
3. Bot-Like Request Patterns:
   • Sending requests at predictable intervals or using static user-agent strings can flag your scraper as a bot. Walmart’s anti-scraping tools are tuned to detect and block automation that doesn’t mimic human behavior.

How to Handle Walmart's 429 Error

Here’s how you can avoid hitting Walmart’s rate limits and resolve the 429 error:

1. Throttle and Randomize Your Requests:

Introduce random delays between your requests, mimicking the timing of real user interactions. Vary the delay between a few seconds to longer intervals to avoid sending requests too quickly.

Why: Walmart’s rate-limiting system detects excessive activity from a single source. Randomizing your request timing helps avoid this, making your scraper appear less automated.

2. Rotate IPs Regularly:

Use a proxy pool to rotate your IP addresses frequently, distributing your requests across multiple IPs. For Walmart, rotating residential or mobile proxies is particularly effective in preventing rate-limit blocks.

Why: Walmart tracks the number of requests coming from each IP. By rotating IPs, you spread the load across different addresses, reducing the likelihood of hitting rate limits and triggering the 429 error.

3. Simulate Human-Like Behavior:

As with other platforms, rotating user-agent strings and headers can help simulate more human-like activity. Additionally, mix in behaviors like scrolling, clicking, or interacting with different parts of the page to make your scraping more natural.

Why: Walmart looks for bot-like patterns, such as identical user-agents or static headers. By simulating real browsing behavior, you reduce the chances of being flagged for automation.

4. Monitor Walmart's Rate Limits:

Keep track of how many requests you are sending per minute/hour and ensure you stay under Walmart’s rate limits. Some sites provide guidance on this, but even without exact numbers, reducing the request frequency can help.

Why: By proactively managing your request rates and monitoring traffic, you can avoid pushing Walmart’s rate limits, which will help prevent the 429 error from occurring.

Simplifying Walmart Scraping with ScraperAPI

Managing Walmart’s rate-limiting defenses can be challenging, but ScraperAPI’s Walmart-specific endpoint offers a tailored solution. ScraperAPI’s Walmart scraper is designed to handle Walmart’s unique anti-scraping measures, allowing you to collect product data at scale without worrying about constant blocks or request limits. It takes care of the complex backend processes so you can focus on gathering the data you need.

Don’t know where to start? Check our guide on scraping Walmart product details with Python.

YouTube Scraping Errors

YouTube 429 Error – Too Many Requests

The 429 error indicates that YouTube’s servers have detected excessive requests from your IP in a short period. This error typically means that you’ve hit YouTube’s rate limits, and your scraper has been temporarily blocked from making further requests.

Common Reasons for YouTube's 429 Error

1. Excessive Request Frequency:
   • Sending too many requests in a short time, whether to video pages or API endpoints, will quickly lead to this error. YouTube closely monitors traffic spikes, especially from single IPs.
2. Lack of IP Rotation:
   • Repeated requests from a single IP address can easily trigger rate limits. YouTube tracks the number of requests coming from each IP, especially if they appear automated.
3. Automated Patterns:
   • Using predictable or repetitive request patterns—like identical user-agent strings or fixed intervals—can reveal that your traffic is being generated by a bot, leading to blocks.

How to Handle YouTube's 429 Error

To avoid or resolve the 429 error on YouTube, consider these strategies:

1. Throttle Your Requests:

Instead of bombarding YouTube with rapid requests, slow down your scraper by introducing random delays between requests. This will help you stay within YouTube’s rate limits.

Why: Throttling your requests spreads them out over time, making them look more like natural user activity rather than bot-driven traffic.

2. Rotate IPs Frequently:

Use a proxy network to rotate IP addresses regularly. For YouTube, rotating residential or mobile IPs works well, as they closely resemble real user traffic.

Why: Rotating IPs allows you to distribute the load across multiple addresses, helping you avoid hitting request caps from a single IP.

3. Use API Alternatives:

Instead of scraping web pages directly, consider using YouTube’s official API, which provides structured access to video metadata, comments, and more. If you're hitting API rate limits, adjust your request volume and monitor YouTube’s daily quota.

Why: The official API offers higher efficiency for certain tasks like gathering video metadata, though it has its own rate limits. Managing your requests carefully can prevent the 429 error from occurring.

YouTube is also sensitive to traffic that consumes large amounts of bandwidth. When scraping video content (as opposed to metadata), ensure you're not requesting too much data simultaneously.

For example, retrieving lower-resolution thumbnails instead of full-resolution versions can significantly reduce bandwidth use, making your scraper less likely to trigger rate limits.

Simplifying YouTube Scraping with ScraperAPI

YouTube’s rate-limiting measures can make scraping challenging, but ScraperAPI offers a streamlined way to navigate them. ScraperAPI helps you avoid the 429 error while scaling your scraping efforts by automating IP rotation, managing headers, and optimizing request timing.

For a more in-depth look, check out our guide on how to scrape YouTube effectively with ScraperAPI. It covers specific strategies and tips for collecting YouTube data efficiently.

Implementing Rate Limiting Handling

Here’s an example of how to handle rate limiting using exponential backoff:

<pre class="wp-block-syntaxhighlighter-code">
  import time
  import requests
  from requests.exceptions import RequestException
  
  def rate_limited_request(url, max_retries=3, initial_delay=1):
      delay = initial_delay
      for attempt in range(max_retries):
          try:
              response = requests.get(url)
              if response.status_code == 429:  # Handling rate limit (HTTP 429)
                  print(f"Rate limited. Attempt {attempt + 1}/{max_retries}. Waiting {delay} seconds...")
                  time.sleep(delay)  # Wait before retrying
                  delay *= 2  # Exponential backoff: double the delay for each retry
              else:
                  response.raise_for_status()  # Raise HTTPError for other 4xx or 5xx
                  return response.text
          except RequestException as err:
              print(f"Error occurred: {err}")  # Catch all request-related errors
              if attempt == max_retries - 1:
                  raise  # Raise final error if max retries are reached
      raise Exception("Max retries reached")
  
  # Usage example
  url = 'https://api.example.com/data'
  try:
      content = rate_limited_request(url)
      print('Request successful!')
  except Exception as e:
      print(f'Failed to retrieve content: {e}')

This rate_limited_request function implements several key features for handling rate limiting:

• It attempts the request multiple times (default 3) before giving up
• If it receives a 429 status code, it waits before retrying
• It uses exponential backoff, doubling the wait time with each retry
• It handles other request exceptions, allowing you to implement different logic if needed

By using this approach, your scraper can automatically adapt to rate limiting, reducing the chance of being completely blocked and improving its overall reliability. You can adjust the max_retries and initial_delay parameters to fine-tune the behavior for specific websites or APIs.

However, the most efficient way to handle rate limits across the biggest sites is by using ScraperAPI. Its advanced bypassing systems use machine learning and years of statistical analysis to distribute your requests through millions of proxies, preventing any rate limits from being triggered while also ensuring the target site doesn’t get overloaded with your requests.

Advanced Bot-Blocker Scraping Error Codes

As we’ve seen with rate limits and common error codes, platforms are becoming increasingly sophisticated in detecting and blocking scraping attempts. However, some websites go one step further, using advanced bot protection mechanisms that make scraping even more challenging. Solutions like Fastly, Akamai, and DataDome have set the bar high for bot detection, introducing complex challenges for scrapers. Understanding how these systems work, identifying their blocks, and finding ways to bypass them is critical for successful web scraping.

Let’s dive into the specific error codes and challenges posed by these advanced bot blockers—and how you can navigate around them.

Fastly

Fastly is a content delivery network (CDN) that integrates advanced bot management solutions to detect and block non-human traffic. While it’s primarily designed for delivering fast, secure content, its bot-detection capabilities can present significant hurdles for web scrapers.

Common Fastly Scraping Error Codes and Messages:

• 403 Forbidden: This error indicates that Fastly’s bot-detection system has flagged your traffic as suspicious.
• 503 Service Unavailable: When Fastly overloads or blocks your IP address, you may encounter a 503 error, preventing access to the target site.

How to Identify Fastly Blocks:

• Response Headers: Look for Fastly-specific headers such as x-served-by or x-cache. These headers can indicate whether Fastly is serving the site and whether the error is being generated by Fastly’s bot protection.
• Sudden IP Blacklisting: Fastly often blocks traffic based on repeated requests from the same IP. A quick block after a burst of scraping activity can be a sign that Fastly’s defenses are in play.

Techniques for Scraping Fastly-Protected Sites:

Rotating IPs and adjusting request timing are key strategies to bypass Fastly's protections. It closely monitors for patterns typical of scraping activity, so varying your traffic can help evade detection.

For more in-depth techniques using ScraperAPI and Python, including code snippets to scrape Fastly-protected websites, check out this guide: How to Scrape Fastly-Protected Websites with Python.

Akamai

Akamai is one of the largest CDNs and security providers on the web. Its bot-detection technology is sophisticated, combining machine learning and behavioral analysis to filter out bots. This makes it a formidable barrier for scrapers.

Common Akamai Scraping Errors:

• 403 Forbidden: When Akamai detects bot-like activity, it returns this standard HTTP error, blocking further access.
• Captcha Challenges: Akamai frequently uses CAPTCHA challenges to verify that traffic is coming from a human user, making it difficult for scrapers to bypass without CAPTCHA-solving capabilities.

How to Detect Akamai's Bot Protection:

• Javascript Challenges: Akamai often requires browsers to execute JavaScript challenges, which can prevent basic scrapers from accessing content.
• Request Patterns: Akamai monitors browsing patterns, so rapid, repetitive requests are a clear trigger for blocks.

Strategies to Bypass Akamai:

Akamai’s system is particularly sensitive to suspicious request patterns, so you’ll need to simulate more human-like activity. This includes slowing down requests, rotating IPs, and using a headless browser like Puppeteer or Selenium to bypass CAPTCHA and JavaScript challenges.

Check out this article, How to Scrape Akamai-Protected Websites with Python, for a deeper dive into scraping Akamai-protected websites and how ScraperAPI can help automate the process.

DataDome

DataDome provides real-time bot detection and protection services to many high-traffic websites. Its system uses AI and behavioral analysis to identify and block scraping attempts, making it one of the more difficult protections to bypass.

Common DataDome Web Scraping Challenges and Errors:

• 403 Forbidden: Like other bot blockers, DataDome often responds with a 403 error when it detects suspicious traffic.
• Captcha and ReCaptcha: One of DataDome’s preferred methods for blocking bots is serving up CAPTCHA challenges to ensure traffic is human.
• Real-Time Blocks: DataDome operates in real-time, meaning you can be flagged and blocked within seconds if your requests trigger its detection algorithms.

How to Detect DataDome Blocks:

• CAPTCHA Flooding: If you encounter CAPTCHA after every request or are constantly presented with ReCaptcha, DataDome is likely the bot blocker at work.
• Bot-Detection Headers: Look for DataDome-specific headers such as X-DataDome-RequestID in the server response, which can indicate a block.

Methods to Bypass DataDome:

Circumventing DataDome requires a multi-faceted approach. Key strategies include rotating IPs, using residential proxies, and incorporating CAPTCHA-solving services. Additionally, headless browsers help bypass JavaScript challenges that basic scrapers can’t handle.

For more details on how ScraperAPI can help you scrape DataDome-protected sites with Python, including code examples, check out this guide: How to Scrape DataDome-Protected Websites with Python.

Implementing User Agent Rotation

Here's a Python snippet demonstrating how to implement user agent rotation.

This technique involves changing the User-Agent header with each request, mimicking different browsers and devices.

  import random
  import requests
  
  user_agents = [
      'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
      'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/14.0 Safari/605.1.15',
      # Add more user agents...
  ]
  
  def make_request_with_random_ua(url):
      headers = {'User-Agent': random.choice(user_agents)}
      return requests.get(url, headers=headers)
  
  for _ in range(5): 
      response = make_request_with_random_ua(url) 
      print(f"Status Code: {response.status_code}, User-Agent:        {response.request.headers['User-Agent']}")

By rotating user agents, you can reduce the likelihood of being detected as a bot by advanced anti-scraping systems. However, remember that sophisticated systems may use other factors to detect bots, so this technique should be used in combination with other strategies like IP rotation and request timing randomization for best results.