Web Scraping with Python & ChatGPT

Sep 25, 2023 · 7 min read

Web scraping is the process of extracting data from websites. This can be useful for gathering large amounts of data for analysis. Python is a popular language for web scraping due to its many scraping libraries and simple syntax. ChatGPT is an AI assistant that can be helpful for generating code and explanations for web scraping tasks. This article will provide an overview of web scraping in Python and how ChatGPT can assist.

Introduction to Web Scraping

Web scraping involves programmatically fetching data from websites. This is done by sending HTTP requests to the target site and parsing the HTML, XML or JSON response. Popular Python libraries for web scraping include:

  • Beautiful Soup - Used to parse and extract data from HTML and XML documents. Allows searching documents using CSS selectors or XPath expressions.
  • Scrapy - A framework for writing crawling spiders to scrape data from multiple sites. Handles requesting, parsing, storing data etc.
  • Selenium - Automates web browsers (Chrome, Firefox etc) for programmatic interaction with sites. Useful when sites rely heavily on JavaScript.
  • Requests - Simplifies making HTTP requests to scrape data from APIs or scrape data from sites by fetching pages.
  • The general workflow for a basic web scraper is:

    1. Send HTTP request to fetch page
    2. Parse text response and extract relevant data
    3. Store scraped data
    4. Repeat for other pages

    This can be extended to scrape various data types, handle pagination, scrape JavaScript generated content, avoid detection etc.

    ChatGPT for Web Scraping Help

    ChatGPT is an AI assistant created by OpenAI to be helpful, harmless, and honest. It can generate natural language explanations and code for a variety of topics. For web scraping, some ways ChatGPT can help are:

    Generating Explanations

    If you are stuck on a web scraping task, ChatGPT can provide explanations of web scraping concepts or specifics for your use case. Some examples:

  • Explain how to use BeautifulSoup to extract all image tags from an HTML document
  • Explain how to handle pagination when scraping data from multiple pages of a site
  • Writing Code Snippets

    You can provide a description of what you want your code to do and have ChatGPT generate starter code snippets for you. For example:

  • Generate python code to scrape headlines from a news site using BeautifulSoup
  • Write a function to parse date strings into datetime objects when scraping dates
  • Be sure to validate any code ChatGPT provides before using it.

    Improving Your Prompts

    If ChatGPT is not providing helpful responses, you can ask it to suggest ways to improve your prompt to get better results.

    Asking Follow-up Questions

    Engage in a back and forth conversation with ChatGPT to get explanations for any follow-up questions you have.

    Explaining Errors

    Share any errors you are getting and ask ChatGPT to explain the issue and how to fix it.

    Web Scraping Example Using ChatGPT

    Let's go through an example of web scraping a Wikipedia page with some help from ChatGPT along the way.

    Goal

    The goal is to get the chronology of the universe located in this Wikipedia page https://en.wikipedia.org/wiki/Chronology_of_the_universe

    There is a nice table in the page that gives you the timeline of events and various epochs…

    Step 1: The first task is to download the page. Lets ask ChatGPT for help here with this prompt…

    give me a script to download this page in python... https://en.wikipedia.org/wiki/Chronology_of_the_universe
    
    import beautiful soup

    ChatGPT should give you this code…

    import requests
    from bs4 import BeautifulSoup
    
    url = "https://en.wikipedia.org/wiki/Chronology_of_the_universe"
    
    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"
    }
    
    response = requests.get(url, headers=headers)
    
    if response.status_code == 200:
        # Parse the page content
        soup = BeautifulSoup(response.text, 'html.parser')
        
        # Save the parsed HTML content to a local file
        with open("chronology_of_the_universe.html", "w", encoding='utf-8') as file:
            file.write(str(soup))
        print("Page has been downloaded and saved successfully!")
    else:
        print("Failed to retrieve the webpage. Status Code:", response.status_code)

    Step 2: Inspect the HTML and look for hints that we can pass to chatGPT. In this case, you can see that the whole table has a class named wikitable

    Step 3: Help out ChatGPT with this info. Here is the prompt

    I want the script to extract a table with the class wikitable - The <th> contains the headings. Extract each row and columns into a csv file

    ChatGPT should return the following code…

    import requests
    import csv
    from bs4 import BeautifulSoup
    
    url = "https://en.wikipedia.org/wiki/Chronology_of_the_universe"
    
    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"
    }
    
    response = requests.get(url, headers=headers)
    
    if response.status_code == 200:
        soup = BeautifulSoup(response.text, 'html.parser')
        
        # Find the first table with class 'wikitable'
        table = soup.find('table', class_='wikitable')
        
        if table:
            # Extract column headers
            headers = [header.get_text(strip=True) for header in table.find_all('th')]
            
            # Open a CSV file for writing
            with open('wikitable.csv', 'w', newline='', encoding='utf-8') as file:
                writer = csv.writer(file)
                
                # Write the headers to the CSV file
                writer.writerow(headers)
                
                # Loop over the rows and write to the CSV file
                for row in table.find_all('tr'):
                    columns = row.find_all('td')
                    if columns:
                        writer.writerow([column.get_text(strip=True) for column in columns])
            
            print('Table has been extracted and saved to wikitable.csv successfully!')
        else:
            print('No table with class "wikitable" found.')
    else:
        print("Failed to retrieve the webpage. Status Code:", response.status_code)

    and when you run it, it creates a CSV file which contains the following data…

    This is neat. right?

    notice the code. It has modified it to include this line…

        table = soup.find('table', class_='wikitable')

    the find function of beautiful soup library is used a lot in web scraping as you can simply tell it to get any element with an ID or class name.

    Best Practices for Web Scraping

    When scraping websites, keep in mind:

  • Respect robots.txt - Exclude pages blocked by the site's robots.txt file
  • Limit request rate - Use throttling to avoid overloading target sites
  • Check terms of use - Avoid violating site terms, scrape ethically
  • Use caches - Cache scraped data to avoid repeat requests
  • Randomize user agents - Use a variety of user agent strings to appear more human
  • Handle errors - Use try/except blocks and other error handling
  • Make structured data - Parse unstructured HTML into structured JSON, CSV etc
  • Store data properly - Use databases, cloud storage etc to store scraped data securely
  • Conclusion

    Some key points:

  • Python has many great libraries for web scraping like Beautiful Soup and Scrapy
  • ChatGPT can be helpful for generating explanations, fixes, and code for web scraping tasks
  • Look at page structure and HTML to understand how to extract the desired data
  • Use tools like regex to parse unstructured text into structured data
  • Follow best practices like respecting robots.txt, rate limiting, and randomizing user agents
  • Web scraping allows gathering valuable data from websites at scale. With Python and a bit of help from ChatGPT, you can build scrapers to extract the information you need.

    ChatGPT heralds an exciting new era in intelligent automation!

    However, this approach also has some limitations:

  • The scraped code needs to handle CAPTCHAs, IP blocks and other anti-scraping measures
  • Running the scrapers on your own infrastructure can lead to IP blocks
  • Dynamic content needs specialized handling
  • A more robust solution is using a dedicated web scraping API like Proxies API

    With Proxies API, you get:

  • Millions of proxy IPs for rotation to avoid blocks
  • Automatic handling of CAPTCHAs, IP blocks
  • Rendering of Javascript-heavy sites
  • Simple API access without needing to run scrapers yourself
  • With features like automatic IP rotation, user-agent rotation and CAPTCHA solving, Proxies API makes robust web scraping easy via a simple API:

    curl "https://api.proxiesapi.com/?key=API_KEY&url=targetsite.com"
    

    Get started now with 1000 free API calls to supercharge your web scraping!

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