DataSpidey is a Python-based web crawler and scraper designed to explore the web and extract specific data from web pages. It's built using the Requests and BeautifulSoup libraries and comes with essential features like error handling, crawl delay, and deduplication.

• Web crawling: Discovers and navigates web pages by following links.
• Web scraping: Extracts specific data from the HTML content of web pages.
• Error handling: Gracefully handles network issues, timeouts, and other exceptions during the crawling and scraping process.
• Crawl delay: Implements a delay between requests to avoid overloading target websites.
• Deduplication: Keeps track of visited URLs to avoid revisiting the same pages and duplicating work.

To use DataSpidey, first install the required libraries using pip:

pip install requests
pip install beautifulsoup4
pip install lxml

To start using DataSpidey, you need to customize the code to suit your specific needs, such as extracting different types of data, handling pagination, or respecting crawl delays specified in robots.txt files.

To run DataSpidey, simply execute the main script with your preferred seed URL:

seed_url = 'https://example.com'
crawl_scrape(seed_url)

To extract different types of data or target specific websites, modify the get_data() function with the appropriate BeautifulSoup selectors and logic.

For example, to extract article titles and URLs from a blog:

def get_data(html):
    soup = BeautifulSoup(html, 'lxml')
    data = []
    for article in soup.find_all('article'):
        title = article.find('h2').text
        url = article.find('a')['href']
        data.append({'title': title, 'url': url})
    return data