Beginning Python - From Novice to Professional

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CHAPTER 15 ■ PYTHON AND THE WEB 319 if tag == 'a' and 'href' in attrs: self.in_link = True self.chunks = [] self.url = attrs['href'] def handle_data(self, data): if self.in_link: self.chunks.append(data) def handle_endtag(self, tag): if tag == 'h4': self.in_h4 = False if tag == 'a': if self.in_h4 and self.in_link: print '%s (%s)' % (''.join(self.chunks), self.url) self.in_link = False text = urlopen('http://python.org/Jobs.html').read() parser = Scraper() parser.feed(text) parser.close() A few things are worth noting. First of all, I’ve dropped the use of Tidy here, because the HTML in the Web page is well behaved enough. If you’re lucky, you may find that you don’t need to use Tidy either. Also note that I’ve used a couple of Boolean state variables (attributes) to keep track of whether I’m inside h4 elements and links. I check and update these in the event handlers. The attrs argument to handle_starttag is a list of (key, value) tuples, so I’ve used dict to turn them into a dictionary, which I find to be more manageable. The handle_data method (and the chunks attribute) may need some explanation. It uses a technique similar to that used in Chapter 22, and which is quite common in event-based parsing of structured markup such as HTML and XML. Instead of assuming that I’ll get all the text I need in a single call to handle_data, I assume that I may get several chunks of it, spread over more than one call. There are several reasons why this may happen—buffering, character entities, markup that I’ve ignored—I just have to make sure I get all the text. Then, when I’m ready to present my result (in the handle_endtag method), I simply join all the chunks together. To actually run the parser, I call its feed method with the text, and then call its close method. This solution is, most likely, more robust to any changes in the input data than the version using regular expressions. Still, you may object that it is too verbose (it’s certainly more verbose than the XPath expression, for example) and perhaps almost as hard to understand as the regular expression. For a more complex extraction task, the arguments in favor of this sort of parsing might seem more convincing, but one is still left with the feeling that there has to be a better way. And, if you don’t mind installing another module, there is . . . Beautiful Soup Beautiful Soup is a spiffing little module for parsing and dissecting the kind of HTML you often find on the Web—the sloppy and ill-formed kind. To quote the Beautiful Soup Web site (http:// crummy.com/software/BeautifulSoup):
320 CHAPTER 15 ■ PYTHON AND THE WEB You didn’t write that awful page. You’re just trying to get some data out of it. Right now, you don’t really care what HTML is supposed to look like. Neither does this parser. —(Beautiful Soup Web site, May 2005) Downloading and installing Beautiful Soup (as of version 2.1.0) is a breeze: Download the file BeautifulSoup.py and put it in your Python path (for example, in the site-packages directory of your Python installation). If you want, you can instead download a tar archive with installer scripts and tests. With Beautiful Soup installed, the running example of extracting Python jobs from the Python Job Board becomes really, really simple—and readable, as shown in Listing 15-3. Listing 15-3. A Screen Scraping Program Using Beautiful Soup from urllib import urlopen from BeautifulSoup import BeautifulSoup text = urlopen('http://python.org/Jobs.html').read() soup = BeautifulSoup(text) jobs = set() for header in soup('h4'): links = header('a', 'reference') if not links: continue link = links[0] jobs.add('%s (%s)' % (link.string, link['href'])) print '\n'.join(sorted(jobs, key=lambda s: s.lower())) I simply instantiate the BeautifulSoup class with the HTML text I want to scrape, and then use various mechanisms to extract parts of the resulting parse tree. For example, I call soup('h4') to get a list of all h4 elements. I iterate over these, binding the header variable to each one in turn, and call header('a', 'reference') to get a list of a child elements of the reference class (I’m talking CSS classes here). I could also have followed the strategy from previous examples, of retrieving the a elements that have href attributes; in Beautiful Soup, using class attributes like this is easier. As I’m sure you noticed, I added the use of set and sorted (with a key function set to ignore case differences) in Listing 15-3. This has nothing to do with Beautiful Soup; it was just to make the program more useful, by eliminating duplicates and printing the names in sorted order. ■Note There are other tools for screen scraping with Python. You might, for example, want to check out Ka-Ping Yee’s scrape.py (found at http://zesty.ca/python). If you want to use your scrapings for an RSS feed (discussed later in this chapter), you can use another tool related to Beautiful Soup, called Scrape ’N’ Feed (at http://crummy.com/ software/ScrapeNFeed).
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Beginning Python From Novice to Pro
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For Ranveig
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viii ■CONTENTS Strings . . . . .
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Introduction A C program is like a
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572 APPENDIX C ■ ONLINE RESOURCES
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Index ■Symbols - operator 558 !=
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