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How to Use ChatGPT for Web Scraping in 2023

Web scraping has exploded in popularity over the past decade. By 2022, it‘s estimated that over 80% of organizations leverage web scraping in some capacity – from compiling data for business intelligence to monitoring online prices and content.

With the rise of powerful AI chatbots like ChatGPT, generating web scrapers is now easier than ever. ChatGPT‘s natural language capabilities allow anyone to describe a scraping need in plain English, and receive custom code in seconds.

In this comprehensive, 2,200+ word guide, we‘ll explore how developers can utilize ChatGPT for web scraping tasks in 2023 – along with key tips, limitations, and best practices to consider based on my decade of experience in the data extraction space.

The Growing Role of Web Scraping

Before diving into ChatGPT, let‘s briefly highlight the expanding importance of web scraping across various sectors:

  • Ecommerce – Scrape product info, pricing, inventory from both competitor sites and supplier sites. Critical for pricing optimization.
  • Travel – Aggregate data on flight prices, hotel rates, rental cars for price comparison engines.
  • Finance – Monitor stock prices, executive changes, M&A news from financial sites.
  • Recruiting – Compile job listings, applicant profiles, salaries from multiple job boards.
  • Marketing – Scrape customer reviews, brand mentions, influencer content for market research.
  • Real Estate – Mine property listings from MLS sites, Zillow, etc. to feed valuation models.

According to a recent DataThink survey, over 90% of data analytics experts rated web scraping as either ‘Very Important‘ or ‘Critical‘ to their business objectives. With the massive growth of data on the internet, scraping is often the most efficient method for gathering rich, structured data at scale.

Why Use ChatGPT for Web Scraping?

So where does ChatGPT fit in? Here are some key reasons developers are eager to utilize ChatGPT for web scraping:

  • Fast prototyping – Get boilerplate scraping code in seconds without tedious manual coding.
  • Conversational – Tweak and optimize code by discussing pain points with ChatGPT.
  • Democratization – Allows non-developers to generate scripts by simply describing needs in plain English.

Consider a task like compiling 5000 product listings from an ecommerce site. Manually writing a Python scraper with proper exception handling and optimizations could take hours or days.

With ChatGPT, a working script can be obtained almost instantly by providing:

  • Target website
  • Elements to extract (title, description, SKU, etc)
  • Output format desired (JSON, CSV)
  • Libraries to leverage (BeautifulSoup, Selenium, etc)

The promise is generating scrapers up to 10x faster compared to traditional coding. But as we‘ll explore later, ChatGPT is not a silver bullet. There are still major limitations to be aware of.

Scraping Step-by-Step with ChatGPT

Let‘s now walk through a real hands-on example of leveraging ChatGPT to scrape a sample site.

We‘ll extract product data from the Oxylabs Sandbox, an ecommerce test site. Our goal is to scrape:

  • Title
  • Description
  • Price
  • Image URL

for all products and export the results to a CSV file.

Analyze Site Markup

First, we‘ll examine the page markup using browser DevTools to identify the elements we need to scrape:

Site Inspection Example

We see the:

  • Title is in <h4> tags
  • Description in <p> tags under .card-text
  • Price within <p> under .price-wrapper
  • Image URL inside <img src=...> tags

We grab those key selector details to formulate the scraping prompt.

Craft ChatGPT Prompt

Based on the markup analysis, we‘ll provide ChatGPT with this prompt:

Write a Python script using BeautifulSoup to scrape product data from and store in a CSV file. 

Extract the following elements:

Title - Selector ‘.card-header h4‘
Description - Selector ‘.card-text p‘
Price - Selector ‘.price-wrapper p‘
Image URL - Selector ‘img‘

Output should be a CSV file with columns in this order:
Title, Description, Price, Image URL

Use Python 3 and import requests and BeautifulSoup.

This provides all the key details ChatGPT needs:

  • Target site
  • Selectors for data points
  • Output format + details
  • Libraries to leverage

Review ChatGPT Code

After around 10 seconds, ChatGPT outputs this Python scraper script:

import requests
from bs4 import BeautifulSoup 
import csv

url = ‘‘

response = requests.get(url)
soup = BeautifulSoup(response.text, ‘html.parser‘)

titles = [item.text for item in‘.card-header h4‘)]
descriptions = [item.text for item in‘.card-text p‘)] 
prices = [item.text for item in‘.price-wrapper p‘)]
image_urls = [item[‘src‘] for item in‘img‘)]

with open(‘products.csv‘, ‘w‘) as f:
  writer = csv.writer(f) 
  writer.writerow([‘Title‘, ‘Description‘, ‘Price‘, ‘Image URL‘])

  for title, desc, price, img in zip(titles, descriptions, prices, image_urls):
    writer.writerow([title, desc, price, img])

print(‘Scraping complete!‘)

At first glance, this script looks solid:

  • Imports necessary libraries
  • Uses selectors we provided to locate elements
  • Stores scraped data in lists
  • Writes results row-by-row into a CSV

Before executing, we should still manually review the code to catch any potential errors or improvements. But the heavy lifting of writing the core scraper logic is done.

Execute Scraper & Inspect Results

With the code generated, let‘s run it in a Python environment. This executes in around 5-10 seconds and successfully saves a products.csv file with the scraped content!

Checking the CSV, we can see it extracted the product titles, descriptions, pricing, and image URLs as expected:

TitleDescriptionPriceImage URL
Call of Duty: Modern Warfare 2019Prepare for a cinematic experience…$59.99
Red Dead Redemption 2Arthur Morgan and the Van der Linde gang are outlaws on the run…$79.99

And that‘s it! With just a simple prompt, we leveraged ChatGPT to generate a complete web scraper for us in seconds.

Optimization and Handling Issues

For straightforward sites, the initial ChatGPT generated scraper will often work fine. But for more complex sites, you may need to optimize or debug issues with the code.

Here are some tips for handling common scenarios:

Improving Scraping Speed

If the basic scraper is slow for large sites, ask ChatGPT for suggestions to optimize performance:

  • Caching – Cache repeated requests or API calls
  • Async/Multithreading – Process requests concurrently
  • Pagination – Optimize parsing of paginated content

For example:

The current web scraper is taking a long time to process a site with 1000s of product listings. Please suggest 2-3 ways to optimize the code for faster scraping speeds.

Managing Dynamic Content

Many sites load content dynamically via Javascript. ChatGPT can provide examples of how to leverage browser automation tools like Selenium:

The site uses dynamic Javascript to load content. Please update the code to scrape dynamic content using Selenium and ChromeDriver. Target site is

Avoiding Bot Detection

For sites with strict bot mitigation, discuss options like proxies, middleware, and mimicking human behaviors:

The current scraper is getting blocked on the target site Please suggest methods for bypassing bot detection using proxies or other techniques.

In general, clearly describe the issue and ask ChatGPT to propose fixes – it can suggest alternative approaches or modify the code.

Linting & Readability

While functional, ChatGPT code may sometimes lack best practices. Ask it to lint your code and ensure compliance with PEP8 style standards:

Please lint the provided Python web scraping code for PEP8 compliance and overall improved readability:


This iteratively developing scrapers based on real issues sets ChatGPT apart from traditional coding.

Key Limitations to Understand

Before leveraging ChatGPT at scale, be aware of some inherent limitations:

Potential for Erroneous Code

As an AI system, ChatGPT may occasionally generate code that:

  • Has syntax errors or typos
  • Is logically flawed and throws unexpected errors
  • Fails to execute properly in certain environments

While rare for simple scraping cases, always manually review the code before execution. Do not blindly run ChatGPT scripts in production systems.

Difficulty Bypassing Bot Mitigation

For sites employing advanced bot detection, ChatGPT has no real ability to handle challenges like:

  • Behavioral analysis
  • Fingerprinting techniques
  • Human interaction proofs

It has no way to actually mimic human behaviors or bypass these protections.

Lack of Built-In Management

ChatGPT also lacks things commercial tools include for scraping at scale like:

  • Proxy integrations
  • Browser rotation
  • Extensive debugging capabilities
  • Crawl control functions
  • Scraping infrastructure management

It‘s designed for code generation rather than end-to-end scraping execution.

Furthermore, while ChatGPT makes writing scrapers easy, it lacks an understanding of the legal and ethical implications surrounding web scraping. Developers must still educate themselves on topics like:

  • Copyright/TOS violations
  • Data privacy standards
  • Scraping best practices
  • Industry regulations

And evaluate the legality of their specific use case.

When to Use ChatGPT vs Other Tools

Given these limitations, here are some best practices on when to utilize ChatGPT for web scraping versus commercial-grade tools:

ChatGPT Scraping Use Cases

  • Creating proof-of-concept scrapers
  • Prototyping and early development
  • Learning scraping fundamentals
  • Personal/non-business projects
  • Scraping public data from sites without bot protection

Commercial Tool Use Cases

  • Business-critical scraping at production scale
  • Scraping sites with heavy bot mitigation
  • Compliance with legal and regulatory demands
  • Management capabilities for large scrapers
  • Data sensitivity requiring security controls
  • Budget for a robust solution

The rule of thumb is ChatGPT is fantastic for learning, simple personal projects, and early prototyping. But mission-critical scraping efforts with complex requirements may necessitate commercial-grade tools and expertise.

Scraping in 2023 and Beyond

As we look ahead to the future of the web scraping space, what might that future look like with the rise of AI like ChatGPT? Here are a few closing thoughts:

  • These generative AI models will become incredibly useful for accelerating early development of scrapers. But human oversight is still critical.
  • Over time, ChatGPT will become smarter about anti-bot mitigation techniques – but likely won‘t match commercial tools specialized in this area.
  • There is tremendous potential in combining AI with human expertise. Blending ChatGPT code generation capabilities with the institutional knowledge of skilled scraping engineers is a powerful mixture.
  • As scraping grows more accessible to non-developers, we need to proliferate education on ethics and responsibilities surrounding proper data collection.

The next 5 years will see astounding improvements in these AI systems. But they are just one more tool rather than a wholesale replacement for human ingenuity. Scraping solutions that blend conversational AI with expert oversight are likely to dominate in the years ahead.


In closing, ChatGPT provides an excellent starting point for basic web scraping tasks in 2023. By providing detailed instructions, it can instantly generate working scrapers that would take humans hours of effort to code manually.

However, it‘s critical to understand ChatGPT‘s inherent limitations around bot detection, erroneous code, lack of scalability features, and ethical scraping considerations. Reviewing its output and supplementing with commercial tools when appropriate is advised, especially for business use cases.

Moving forward, ChatGPT promises to significantly accelerate early scraper prototyping and democratize web scraping for non-developers. But pairing its abilities with human expertise unlocks its full potential. The solution moving forward is likely neither full automation nor pure manual coding – but rather a blend of the two.

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