Web scraping has become an essential tool for businesses and individuals who regularly need to gather data from multiple sources. Unfortunately, web scraping can be intimidating for beginners. However, LLM-based tools made it easy for beginners to learn web scraping. LLMs can be considered unpaid interns or tuition teachers.

The most popular LLM-based tool on the planet, Chat GPT, is one of the most valuable resources for beginners in web scraping, providing guidance and support as they navigate the process. With the help of Chat GPT, beginners can quickly and effectively scrape data from websites and gain insights that can inform their decision-making.

Beginners can ask Chat GPT questions about web scraping and receive helpful responses to guide them through the process. Experienced people can use it to get their job done faster. At Datahut, we use Chatgpt and Github Copilot to get our jobs done faster and more efficiently.

For example, beginners can ask Chat GPT how to scrape data from a specific website, what tools and technologies to use, and how to clean and analyze the data after web scraping.

Chat GPT can provide detailed and easy-to-understand explanations, making it easier for beginners to learn and apply web scraping techniques. This can help beginners build their knowledge and confidence in web scraping, leading to more accurate and efficient data acquisition.

In this blog, we will explore how to ask more accurate questions to learn web scraping coding quickly from Chat GPT. And as an example, we show you how you can scrape the Amazon site using ChatGPT.

Steps Involved in Web Scraping

Before beginning the web scraping coding, let’s look at the steps involved.

  1. Identify the target website: The first step in the web scraping process is to identify the data source, which is the website in our case.

  2. Choose a web scraping tool: Multiple web scraping libraries are available for developers. You must select a web scraping tool or library that suits your needs. Some popular web scraping tools include BeautifulSoup, Scrapy, Selenium, and Playwright. Here is a list of 33 web scraping tools.

  3. Inspect the website: You need to understand how the data is being shown on the website to check If the data is being loaded dynamically. You also need to understand the website structure you want to scrape. Use your web browser’s developer tools to inspect the HTML and CSS code.

  4. Build a web scraper: Write a script to extract the data after selecting the library to scrape the data. Here are the steps for building the web scraper.

  5. Set up your scraper development environment: Install the chosen scraping tool or library on your local machine. Set up your development environment and get ready.

  6. Fetch the HTML content for the target website: Write a function to send a request to the target website and fetch the HTML content of the desired web page. Ensure you have systems for handling request timeouts and other possible scenarios.

  7. Parse the HTML content using a parser library: Parse the HTML content using the parsing library of your web scraping framework to extract the specific data attributes you’re trying to access.

  8. Dealing with Pagination: If the data you need is spread across multiple pages, you must handle pagination or require interaction (e.g., clicking buttons or filling out forms). This may involve analyzing the website’s URL structure, submitting form data, or following links to subsequent pages.

  9. Handle anti-scraping measures and other issues: Some websites deploy anti-scraping technologies to prevent web scrapers. They use techniques such as timing delays, slow page loading, lazy loading of content, etc. To avoid detection or overcome these measures, you may need to implement additional strategies such as proxies, rotating user agents, or introducing delays between requests.

  10. Test the scraper: Run the web scraper on a small subset of the data to ensure it extracts the right information you need. If there are any issues – correct it.

  11. Run the web scraper on a production server: Run the web scraper on a server or a production environment.

  12. Store the data: Write it into a database or export it into a suitable format like csv or json.

  13. Clean and process the data: Depending on your use case, you may need to clean and preprocess the data before using it for analysis or other purposes.

  14. Monitor the website: If you plan to scrape the website regularly, set up a monitoring system to check for changes in the website’s structure or content.

  15. Respect website policies: Follow the website’s terms of service and data policies. Do not overload the website with requests; avoid scraping sensitive or personal information.

Chat GPT will assist you in navigating through each step mentioned above. When requesting assistance, please provide precise information to receive correct and relevant answers. Start by specifying the website from which you wish to scrape data. You can either provide the URL or describe the website’s structure and content to help the chatgpt understand the task better. Additionally, clearly state the specific data you want to extract, including elements, sections, or patterns of interest if you have a preferred web scraping tool or library, such as BeautifulSoup or Scrapy, specify that as well.

Alternatively, you can leave the choice open-ended, and ChatGPT will suggest a suitable library based on your task requirements. If you have any additional requirements or constraints, such as pagination handling, dynamic content handling, or proxy usage, please include them in your query. These details will help us generate more accurate and relevant code.

It is essential to understand the different types of websites based on their characteristics and behavior before starting the web scraping process. These include:

  • Static Websites: These websites have fixed content that does not change frequently. The HTML structure remains the same each time you visit the site.

  • Dynamic Websites: These websites generate content dynamically using JavaScript, AJAX, or other client-side technologies. The content may change based on user interactions or data retrieved from external sources.

  • Websites with JavaScript Rendering: These websites heavily rely on JavaScript to render content dynamically. The data may be loaded asynchronously, and the HTML structure may undergo modifications after the initial page load.

  • Websites with Captchas or IP Blocking: These websites implement Captchas or block IP addresses to prevent automated scraping. Additional measures are required to overcome these obstacles during the scraping process. Approaching a professional web scraping company would be the way to go, as chatgpt won’t be of much use here.

  • Websites with Login/Authentication: These websites require user login or authentication to access specific data. Proper authentication techniques must be employed to access and scrape the desired content.

  • Websites with Pagination: These websites display data across multiple pages, typically using pagination links or infinite scrolling. Special handling is necessary to navigate through and scrape content from multiple pages.

It is essential to consider these characteristics and behaviors when selecting the appropriate web scraping techniques and tools. Each situation may require different approaches and tools to retrieve the desired data effectively. BeautifulSoup is a popular Python library for scraping static websites, offering efficient parsing and navigation of HTML/XML documents. With its simplicity and powerful parsing capabilities, BeautifulSoup is well-suited for scraping static websites and extracting desired data efficiently.

On the other hand, for dynamic websites that generate content using JavaScript, AJAX, or other client-side technologies, Selenium is a valuable tool. Selenium is a widely-used web automation framework that allows you to control web browsers programmatically. It enables you to interact with dynamic elements, simulate user actions like clicks and form submissions, and retrieve the rendered HTML content after the JavaScript has been executed. This makes Selenium an excellent choice for scraping dynamic websites where traditional parsing libraries like BeautifulSoup may not be sufficient.

When dealing with more complex scenarios, such as websites with JavaScript rendering, you might consider using libraries like Playwright. Playwright is a powerful automation library that provides a unified API to control multiple web browsers, including Chromium, Firefox, and WebKit.

In this tutorial, we have selected Amazon as our e-commerce website to demonstrate web scraping using Chatgpt. To scrape Amazon effectively, it may be necessary to use advanced web scraping tools that are capable of handling dynamic content. Some suitable options include using Beautiful Soup with requests-HTML, Selenium, Scrapy, and Playwright.

For example, we will target the Amazon product page for toys for kids. The target web page contains product details such as titles, images, ratings, and prices.

Also Read: How to Build an Amazon Price Tracker using Python

Scraping Amazon website with Chat GPT

The first step in web scraping is to extract product URLs from an Amazon webpage. To accomplish this, it is necessary to identify the URL element on the page that corresponds to the desired product. First, we need to check the structure of the webpage. To inspect components, right-click on any component of interest and select the “Inspect” option from the context menu. This will allow us to analyze the HTML code and find the data needed for web scraping.

Automate Web Scraping Using ChatGPT: How to Scrape Amazon using Chatgpt

To generate the code, left-click on the content of the corresponding URLs and copy it. Here we will be utilizing Beautiful Soup for web scraping.

Automate Web Scraping Using ChatGPT: How to Scrape Amazon using Chatgpt
Automate Web Scraping Using ChatGPT: How to Scrape Amazon using Chatgpt

The code generated by Chatgpt will extract the URLs of products listed under the category of “toys for kids.”

The program begins by importing the necessary libraries, requests, and BeautifulSoup. The base URL is set to the Amazon India search page for toys for kids. The program sent a request to the base URL using the Python requests library.

The response to the request is stored in the ‘response’ variable. Then, a Beautiful Soup object is created from the response content, using the HTML parser library as the parser. We can use other parsers such as lxml as well, but for this, let’s stick with html parser.

The program first generates a CSS selector that can locate the URL. Then it uses BeautifulSoup’s ‘find_all’ method and searches for all anchor elements (links) with a CSS selector. These are the elements that contain the URLs of the products on the page.

We initiate an empty list named ‘product_urls’, to store the extracted URLs. A for loop is then executed to iterate through each element in ‘product_links’. For each element, the ‘href’ attribute is extracted using BeautifulSoup’s ‘get’ method. If a valid ‘href’ is found, the base URL is appended to it, forming the complete URL of the product. This full URL is then added to the ‘product_urls’ list. Finally, the program prints the list of extracted product URLs- just to be sure.

In this use case, a CSS selector is used to locate the element from the Amazon product page. There are alternate ways to do that, like using an XPath. Some developers prefer Xpaths over CSS selectors. If you prefer to use XPath, reference “using XPath” in your initial prompt to Chatgpt.

Here is a quick tutorial on using Xpaths: Xpaths for web scraping

Automate Web Scraping Using ChatGPT: How to Scrape Amazon using Chatgpt
Automate Web Scraping Using ChatGPT: How to Scrape Amazon using Chatgpt

The category we’re scraping contains a lot of products with unique product urls that differentiate each product. Our objective is to scrape data from these individual pages (known as product description pages). We will solve the pagination problem by l inspecting the next button and copying the content to prompt chatgpt

Automate Web Scraping Using ChatGPT: How to Scrape Amazon using Chatgpt
Automate Web Scraping Using ChatGPT: How to Scrape Amazon using Chatgpt
Automate Web Scraping Using ChatGPT: How to Scrape Amazon using Chatgpt

The above code is an extension of the first code snippet. We’re just extending it to scrape all the product URLs from multiple pages of the search results on Amazon. In the first part of the code, only the product URLs from the category pages were extracted. However, the second code snippet introduces a while loop to iterate through multiple pages to get around the pagination.

The loop continues until there is no “Next” button on the page, indicating that all available pages have been scraped. The code checks if there is a “Next” button on the page using BeautifulSoup’s find method. If a “Next” button is found, the URL for the next page is extracted and assigned to the next_page_url. The base URL is then updated to next_page_url, allowing the loop to continue onto the next page. If no “Next” button is found, indicating that it is the last page of the search results, the loop breaks, and the script prints the complete list of all the product URLs scraped.

After successfully navigating through an Amazon category, the next step is to extract the product information for each product. To do this, we need to examine the structure of the product page. By inspecting the webpage, we can identify the specific data we need for web scraping. By locating the appropriate elements, we can extract all the desired information and proceed with our web scraping process.

Let’s explore how to scrape product names.

Automate Web Scraping Using ChatGPT: How to Scrape Amazon using Chatgpt

Typically, we inspect the product names and copy the content of product names as usual.

Automate Web Scraping Using ChatGPT: How to Scrape Amazon using Chatgpt
Automate Web Scraping Using ChatGPT: How to Scrape Amazon using Chatgpt

Here, the code snippet enhances the web scraper by extracting not only the product URLs but also the product names. Additionally, it uses the Pandas library to create a data frame from the collected data and save it to a CSV file. In the second code snippet, after appending each product URL to the product_data list, the code sends a request to the product URL and then finds the element containing the product name. The product name is extracted and appended to the product_data list along with the product URL. Once the scraping process is complete, we use Pandas to create a DataFrame from the product_data list. This dataframe organizes the product URLs and names into columns. Finally, the data frame is saved to a CSV file named ‘product_data.csv.’

Likewise, we can extract the price of each product. Typically, we inspect the price and copy the content of the price as usual.

Automate Web Scraping Using ChatGPT: How to Scrape Amazon using Chatgpt
Automate Web Scraping Using ChatGPT: How to Scrape Amazon using Chatgpt
Automate Web Scraping Using ChatGPT: How to Scrape Amazon using Chatgpt

Similarly, we can extract all other product information such as rating, number of reviews, image, etc.


Automate Web Scraping Using ChatGPT: How to Scrape Amazon using Chatgpt

Number of Reviews:

Automate Web Scraping Using ChatGPT: How to Scrape Amazon using Chatgpt


Automate Web Scraping Using ChatGPT: How to Scrape Amazon using Chatgpt
Automate Web Scraping Using ChatGPT: How to Scrape Amazon using Chatgpt

Let’s standardize the code for better readability, maintainability, and efficiency without altering its external behavior.

Automate Web Scraping Using ChatGPT: How to Scrape Amazon using Chatgpt

Also Read: A Guide to Scrape Indeed using Selenium and BeautifulSoup

Limitations of using ChatGPT for web scraping

When requesting ChatGPT to generate code for web scraping, there are several limitations to be aware of:

1. Limited Contextual Understanding

ChatGPT has limited contextual understanding beyond a few preceding messages. It may not be aware of the specific website or preferred web scraping libraries, which can result in code that doesn’t precisely align with your requirements.

2. Accuracy and Error Handling

The generated code may not always be accurate or error-free. ChatGPT’s responses are derived from patterns and examples in its training data, and there is a possibility of syntax errors or code that doesn’t function as intended. Additionally, the code may lack comprehensive handling of edge cases or effective error handling.

3. Limited Knowledge of Recent Advances

ChatGPT’s training data is current up until September 2021, so it may not be aware of the most recent advancements in web scraping libraries, techniques, or changes in website structures or APIs. This can result in code generation that is less accurate or incomplete for newer technologies. It is common to see depreciation warnings and errors when running code generated by Chatgpt.

4. Adherence to Best Practices

The generated code may not adhere to best practices or employ the most efficient implementation strategies. It’s important to review and optimize the code for performance, readability, and maintainability. Additionally, it may lack robust error handling or fail to account for all potential edge cases, so appropriate error-handling mechanisms should be incorporated.

5. Tool and Library Recommendations

While ChatGPT may suggest specific web scraping tools or libraries based on the information provided, it may not consider all available options or your project’s specific requirements. It’s essential to conduct your own research and choose the appropriate tools or libraries based on your needs.

6. Complex or dynamic websites

Web scraping can become challenging when dealing with complex or dynamically generated web pages. ChatGPT might generate code that works for simple websites but fails to handle dynamic content, JavaScript rendering, or CAPTCHAs.

7. Limited Back-and-Forth Dialogue

ChatGPT operates on a message-response basis, which limits its ability to engage in a back-and-forth dialogue to fully understand and refine your specific requirements. This can result in code generation that is less accurate or incomplete.

8. Legal and Ethical Considerations

Web scraping may have legal restrictions or be against the terms of service of certain websites. It’s essential to ensure compliance with applicable laws, regulations, and website policies. Obtain proper permissions if required and respect the website’s terms of use.

ChatGPT generates just a basic web scraper, and it may not be ideal for production-level usage. Considering these limitations, it’s essential to use the generated code as a starting point, carefully review it, and make necessary modifications according to best practices, specific requirements, and the latest web technologies. To enhance the code, it’s advisable to leverage your own expertise and conduct additional research. Additionally, it is crucial to be mindful of legal and ethical considerations when engaging in web scraping activities.

If you are a beginner looking to learn web scraping or need a basic one-time copy project, asking ChatGPT can be a suitable option. However, if you require regular data extraction or prefer not to spend significant time on web scraping code, it is recommended to seek assistance from a professional company like Datahut that specializes in web scraping.

Wrapping up

Web scraping has become essential for data gathering, but it can be intimidating for beginners. LLM-based tools like ChatGPT have made web scraping more accessible.

Chat GPT provides guidance and support, helping beginners scrape data effectively. It offers detailed explanations and helps build knowledge and confidence in web scraping. By following the steps involved in web scraping and utilizing the appropriate tools like BeautifulSoup, Selenium, or Playwright, beginners can extract data from websites and make informed decisions. While Chat GPT has its limitations, it serves as a valuable resource for beginners and experienced users alike.

Looking for reliable web scraping services for your data needs? Contact Datahut today!