Web scraping is a powerful tool, allowing you to extract data from websites for analysis, insight, and innovation. But how do you get started, particularly if you’re not already a programmer? That’s where we come in. Today, we’re going to walk you through a practical example of web scraping using two accessible and potent tools: Replit and ChatGPT.
We’ll explain exactly how to scrape websites with ChatGPT and Replit.
In this article, we’ll be showing how to scrape websites, leveraging both these tools to scrape the popular gaming website Steam, extracting the titles of its top-selling games.
Replit is a user-friendly online coding environment, perfect for beginners and seasoned coders alike. ChatGPT is a sophisticated AI developed by OpenAI that can understand and generate human-like text.
This step-by-step guide is designed to make the process easy to understand and follow. We’ll be demystifying each step, explaining the reasoning behind it, and ensuring you have all the information you need to conduct your own web scraping projects.
Whether you’re seeking to scrape data for personal interest, to gain insights, or to fuel business decisions, this guide will equip you with the necessary skills.
So let’s dive right in and explore the fascinating world of web scraping with AI!
How to Scrape Websites with ChatGPT and Replit
If you’re not already a programmer, scraping a website can seem to be a daunting task. But by harnessing ChatGPT and Replit, the process is surprisingly simple! To illustrate this, we’ll go step by step through the process, in this case using GPT4 and Replit to scrape the titles list of the top sellers on the Steam Store webpage.
You should be able to apply this process to essentially any website you want to scrape.
Defining Key Terms
Before we get started with the hands-on aspect of web scraping, it’s essential to make sure we’re all on the same page with the terms we’ll be using throughout this guide. Even if you have some familiarity with these concepts, a quick refresher can’t hurt!
Artificial Intelligence (AI) and ChatGPT
Artificial Intelligence, or AI, is a field of computer science that focuses on creating systems capable of performing tasks that typically require human intelligence. These tasks include understanding natural language, recognizing patterns, and making decisions.
One exciting development in AI is ChatGPT, a model developed by OpenAI. ChatGPT is an LLM that uses a technique called machine learning, where it learns patterns from large amounts of text data. This learning process allows it to generate human-like text that’s contextually relevant to the input it’s given.
In this guide, we’ll be using ChatGPT 4 to help us with our web scraping project. That said, you should still be able to accomplish all these tasks if you’re using the free GPT3.5 version as well.
Web Scraping
Web scraping is a method used to extract data from websites. It involves making a request to a website (like asking the website to show its contents to you), then parsing the HTML of that site to find and extract the data you’re interested in.
Web scraping can be a powerful tool for gathering data, but it’s important to note that you should always respect the website’s terms of service and only scrape public data.
Replit
Replit is an online coding platform. What makes Replit so great, especially for beginners, is its simplicity and convenience. You don’t need to install anything on your computer to use it – everything happens directly in your web browser. It supports many programming languages, including Python, which we’ll be using for our web scraping project.
Now that we’ve defined our key terms, we’re ready to dive into the practical side of things. In the next section, we’ll show you how to get started with Replit and set up your first Python project.
Getting Started with Replit
Now that we’re familiar with the terms we’ll be using, let’s dive into the practical side of things. The first step is to set up an account on Replit. Here’s how you can do it:
Setting up an Account on Replit
- Head over to Replit and click on the “Sign up” button at the top right corner of the page.
- You’ll be asked to provide a username, email, and password. Alternatively, you can choose to sign up with a Google, GitHub, or Facebook account.
- Once you’ve filled in your details, click the “Sign up” button. You’ll be taken to your Replit dashboard, where you can start creating and managing your coding projects.
Understanding the Replit Interface
Before we start coding, let’s familiarize ourselves with the Replit interface:
- Editor: This is where you’ll write your code. The editor supports syntax highlighting, which means it will color different parts of your code to make it easier to read and understand.
- Console: The console is where your code gets executed. When you run your code, the output will be displayed in the console.
- File tree: On the left side of the screen, you’ll find the file tree. This is where you can manage all the files in your project.
Creating Your First Python Project on Replit
Now that you have your account set up and are familiar with the interface, let’s create your first Python project:
- Click on the “+ New Repl” button on the top right of your dashboard.
- A pop-up window will appear. Select “Python” as the language.
- Give your Repl a name. This could be anything, but for this guide, let’s name it “SteamTopSellersScraper”.
- Click the “Create Repl” button.
Congratulations, you’ve just set up your Replit account and created your first Python project! In the next section, we’ll introduce ChatGPT in more detail and set up the OpenAI API in Replit.
ChatGPT 101
We assume that you’re already familiar with ChatGPT, but there are a few things we should touch on regarding this particular task.
ChatGPT, developed by OpenAI, is an AI language model known for its ability to generate human-like text. It understands context and can respond to prompts with relevant information. For our web scraping project, ChatGPT can be an invaluable assistant, providing insights and generating code snippets to help us interact with the data we scrape from the Steam website.
It’s important to understand that while ChatGPT’s responses can be incredibly accurate and human-like, it doesn’t comprehend text like a human. Instead, it predicts responses based on patterns it learned during its training.
Accessing ChatGPT
There are two main ways to access ChatGPT’s assistance:
- Through the OpenAI API: This method requires an API key, which acts as a password, allowing your coding project to communicate directly with ChatGPT. This API key can be obtained by signing up on the OpenAI website. Once you have it, you can use the key within your Replit project to directly interact with ChatGPT.
- Switching between browser windows: If you don’t have access to an OpenAI API key, don’t worry, you can still get help from ChatGPT! You can use two browser windows, one open to Replit and one to ChatGPT. When you need assistance or a code snippet, simply ask ChatGPT, then copy and paste the response back into your Replit project.
Each method has its own advantages. Using the API provides a more integrated experience, but switching between browser windows is a straightforward and accessible way to leverage ChatGPT’s capabilities.
In the next section, we’ll dive into setting up your Replit environment for web scraping and interacting with ChatGPT. Whether you’re using the API or switching between browser windows, the adventure is just beginning!
Setting Up Your Replit Environment
Now that we’re familiar with ChatGPT and how to access it, let’s set up our Replit environment for this particular web scraping project. Regardless of how you’re accessing ChatGPT, you’ll need to prepare your Replit environment the same way.
In order to scrape with ChatGPT, I had to ask ChatGPT how to do it! It explained that I needed to use two libraries: requests and BeautifulSoup.
Remember that it’s always a good idea to chat with ChatGPT, ask it questions, ask it to elaborate, and when something goes wrong, ask how to fix it. Unless you’re not already a programmer, get used to doing this when working with ChatGPT on anything coding-related.
Remember that it’s always a good idea to chat with ChatGPT, ask it questions, ask it to elaborate, and when something goes wrong, ask how to fix it. Unless you’re not already a programmer, get used to doing this when working with ChatGPT on anything coding-related.
Now let’s explain what requests and BeautifulSoup are, and how to use them.
requests: This library allows us to send HTTP requests. We’ll use it to request the web page that we want to scrape.BeautifulSoup: This library is used for parsing HTML and XML documents. It creates parse trees that are helpful to extract the data easily.
To install these libraries, you go to the Packages icon on Replit, and search for both “Requests” and “BeautifulSoup”. Install the most recent version of each of them.
Then add the following lines of code at the top of our Replit Python file:
import requests from bs4 import BeautifulSoup
Again, I had no idea that I had to do this. Through discussion with ChatGPT, I learned these steps.
Setting Up Interaction with ChatGPT
If you’re using the OpenAI API to interact with ChatGPT, you’ll need to install the openai Python library. You can do this by adding the following line to the top of your Python file:
import openai
You’ll also need to set your OpenAI API key. You can do this by adding the following line:
openai.api_key = 'your-api-key'
Replace 'your-api-key' with the actual key you received from OpenAI. Be careful to keep this key secret, as anyone who has it can use your OpenAI account!
If you’re switching between browser windows, you don’t need to worry about setting up an API key.
Sending Your First Web Scraping Request
Now that we’ve prepared our environment, it’s time to start the actual web scraping process. Our first step is to send a request to the website we want to scrape. In this guide, we’ll be scraping the top sellers page of the Steam website.
Using Chrome Developer Tools to Identify HTML Elements
Before we can extract data from a webpage, we need to know where that data is located within the page’s HTML. The Google Chrome browser has a handy set of Developer Tools that can help us with this.
To open the Developer Tools, right-click anywhere on the webpage and select “Inspect” from the context menu. This will open a panel on the right side of your browser displaying the HTML of the webpage.
The panel has two main parts: the HTML viewer on the left, and the CSS and JavaScript viewer on the right. For our purposes, we’ll be focusing on the HTML viewer.
You can navigate through the HTML by clicking on the arrow icons next to the HTML tags. When you hover over an HTML element in the Developer Tools, the corresponding part of the webpage will be highlighted.
In our case, we’re interested in the titles of the top selling games on Steam. To find the HTML tag and class for the titles, follow these steps:
- Open the Steam top sellers page and start the Developer Tools as described above.
- In the HTML viewer, navigate to a part of the page that displays a game title.
- Hover over different HTML elements until the game title on the webpage is highlighted. Click on the arrow next to these elements to expand them and see more detail.
- Once you’ve found the element that corresponds to the game title, look at the tag name and the class name. In this case, we find that the tag name is
divand the class name istitle.
Now that we know the tag and class for the game titles, we can use this information to extract the titles in the next section.
In web scraping, this process of finding the correct HTML elements is often one of the most time-consuming parts, but it’s also one of the most crucial. Good job on getting through it!
Ask ChatGPT for the scraping code
I asked ChatGPT to provide the scraping code, using the BeautifulSoup and requests libraries. ChatGPT responded with the following code block, telling me to place the code in the main.py file.
import requests
from bs4 import BeautifulSoup
# URL to scrape
url = "https://store.steampowered.com/search/?filter=topsellers"
# use requests to fetch the URL
response = requests.get(url)
# parse the content with BeautifulSoup
soup = BeautifulSoup(response.content, 'html.parser')
# find all the span tags with the class 'title'
tags = soup.find_all('span', class_='title')
# create a list to hold the game titles
game_titles = []
# iterate over each tag
for tag in tags:
# get the text inside this tag and add it to the list
game_titles.append(tag.text)
# print out the game titles
for title in game_titles: print(title)Click on the RUN button on the top of Replit, and (assuming all went well) you should get an output that looks something like this:
Counter-Strike: Global Offensive
Street Fighter™ 6
Steam Deck
Destiny 2
The Outlast Trials
Call of Duty®: Modern Warfare® II
Red Dead Redemption 2
The Elder Scrolls® Online
Starship Troopers: Extermination
System Shock
Rust
Apex Legends™
War Thunder
Tom Clancy’s Rainbow Six® Siege
Grand Theft Auto V
Gunfire Reborn
Lost Ark
Warframe
NBA 2K23
Arma 3
STAR WARS Jedi: Survivor™
Cities: Skylines
The Sims™ 4
Dead by Daylight
Valve Index® Headset
Sniper Elite 5
Hogwarts Legacy
ELDEN RING
FINAL FANTASY XIV Online
Friends vs Friends
Sons Of The Forest
Marvel’s Spider-Man: Miles Morales
Destiny 2: Lightfall
Marvel’s Spider-Man Remastered
Gunfire Reborn – Artisan and Magician
EA SPORTS™ FIFA 23
Crusader Kings III
Ready or Not
MOBILE SUIT GUNDAM BATTLE OPERATION 2
Call of Duty®: Black Ops III
Project Zomboid
Team Fortress 2
SMITE®
Stellaris
Travellers Rest
Sea of Thieves 2023 Edition
Hunt: Showdown
Black Desert
The Elder Scrolls Online: Necrom
Hi-Fi RUSH
Hooray, it worked! If you get an error, simply copy the error into ChatGPT and ask how to fix the error. It may take a couple of iterations, but with a little perseverence, you’ll get it done (see below for more details on troubleshooting).
Congratulations!
And with that, you’ve successfully scraped the titles of the top selling games on the Steam website! This is a big milestone in your web scraping journey. In the next section, we’ll discuss some of the challenges and considerations when web scraping. So stay tuned, we’re not done yet!
Understanding the Limitations and Ethics of Web Scraping
Web scraping is a powerful tool, but it’s not without its challenges and ethical considerations. In this section, we’ll discuss some of the common limitations you might encounter when scraping websites, as well as the ethical guidelines you should follow.
Limitations
- Website Structure Changes: Websites frequently update their layout and design, which can break your scraping code. If the tags or classes you’re targeting with BeautifulSoup change, you’ll need to update your code accordingly.
- Dynamic Content: Some websites load content dynamically using JavaScript. This means the content doesn’t exist in the HTML when the page initially loads, making it difficult to scrape. There are ways to scrape dynamic content, but they’re more complex than what we’ve covered in this article.
- Rate Limiting and Bans: Websites don’t always appreciate being scraped. Some sites have measures in place to detect and block web scrapers, such as rate limiting (limiting the number of requests a single IP address can send in a certain time period) or outright IP bans.
Ethics
- Respecting the Robots.txt File: Websites use a file called robots.txt to communicate which parts of the site are off-limits to web crawlers and scrapers. Always check this file before scraping a website, and respect the site’s wishes.
- Not Overloading Servers: Sending too many requests to a website in a short period of time can overload the server and negatively impact the site’s performance. Be considerate and space out your requests.
- Privacy: Be mindful of privacy considerations when scraping and using data. Don’t scrape or use personal data without consent.
Web scraping is a potent tool that can unlock a lot of data on the internet. However, it’s important to understand its limitations and use it responsibly. Always respect the rules of the site you’re scraping and the privacy of individuals.
Leveraging ChatGPT for Troubleshooting
Coding is often a process of solving one problem after another, and web scraping is no exception. You’ll likely encounter issues and errors that you’ll need to troubleshoot. Luckily, ChatGPT can be an invaluable assistant in this process.
Asking ChatGPT for Help
If you’re stuck on a problem or not sure how to proceed, you can ask ChatGPT for help. Simply describe your problem or question in as much detail as possible, and ChatGPT will generate a response. Copy any errors that show up in your developer console.
For example, if you’re getting an error message when you try to send a request to a website, you could ask ChatGPT, “I’m getting a 403 error when I try to send a GET request to a website with the requests library in Python. What does this mean and how can I fix it?”
ChatGPT will understand your prompt and provide a response, explaining what a 403 error is and suggesting ways to fix it.
Asking for Code Snippets
ChatGPT can also generate code snippets that you can use in your project. For instance, you could ask, “How do I extract all the links from a webpage with BeautifulSoup in Python?” and ChatGPT will provide a code snippet showing how to do this.
Remember, ChatGPT is here to assist you. Don’t hesitate to ask for help when you need it.
Wrapping Up and Next Steps
Congratulations! You’ve journeyed through the process of setting up your environment in Replit, sending your first web request, parsing a webpage with BeautifulSoup, extracting data, and even troubleshooting with ChatGPT. You’ve taken a significant step into the world of web scraping and automated data extraction.
But this is just the beginning. Here are some potential next steps for you to consider:
1. Experiment with Other Websites
Try scraping data from other websites. Each site is unique, and scraping different sites will give you a broader understanding of how web scraping works. Remember, always respect a website’s robots.txt file and avoid overloading the server.
2. Learn More About BeautifulSoup
BeautifulSoup is a powerful library with many more features than we’ve covered here. You can learn more from the official BeautifulSoup documentation.
3. Explore Other Python Libraries for Web Scraping
There are other Python libraries for web scraping that you might find useful, such as Scrapy or Selenium. Each library has its strengths and weaknesses, so experiment and find what works best for you.
4. Keep Asking Questions
Continue using ChatGPT as a resource for learning and troubleshooting. The more specific and detailed your questions, the better ChatGPT can assist you.
5. Check Out LangChain
LangChain is a powerful Python-based tool that can tie AI chatbots to various traditional applications like Google Drive, various websites, etc. It’s like an API on steroids, and is worth checking out.
Web scraping is a powerful tool in the world of data analysis, and you’ve taken the first steps towards mastering it. Whether you want to track prices on e-commerce sites, monitor news articles, or analyze trends on social media, the skills you’ve learned here will serve you well.
Remember, learning to code and web scrape is a journey, full of challenges and rewards. Don’t be discouraged by the obstacles you encounter. Keep experimenting, keep learning, and most importantly, keep having fun.
Good luck on your web scraping adventures!
If you want to get more ambitious and try using ChatGPT as your own personal software developer, see this article:
How I Used ChatGPT to Develop a Web App Without Coding Skills
