[kmike84]

Scrapy spider that is doing exactly the same::

    # It requires scrapy from github.
    # Save it to tickets.py and execute 
    # "scrapy runspider tickets.py" from the command line

    from urlparse import urljoin
    import scrapy
    
    class TicketSpider(scrapy.Spider):
        name = 'tickets'
        start_urls = ['http://philadelphia.craigslist.org/search/sss?sort=date&query=firefly%20tickets']
    
        def parse(self, response):
            for listing in response.css('p.row'):
                price_txt = listing.css('span.price').re('(\d+)')
                if not price_txt:
                    continue
                price = int(price_txt[0])
                if 100 < price <= 250:
                    url = urljoin(response.url, listing.css('a::attr(href)').extract()[0])
                    print ' '.join(listing.css('::text').extract())
                    print url
                    print

There is no reason to prefer Scrapy for extracting information from a single webpage, but on the other hand it is not any harder than BS+pyquery+requests.