Chapter 2. Scraping the Data

In the preceding chapter, we built a crawler that follows links to download the web pages we want. This is interesting but not useful—the crawler downloads a web page, and then discards the result. Now, we need to make this crawler achieve something by extracting data from each web page, which is known as scraping.

We will first cover a browser extension called Firebug Lite to examine a web page, which you may already be familiar with if you have a web development background. Then, we will walk through three approaches to extract data from a web page using regular expressions, Beautiful Soup and lxml. Finally, the chapter will conclude with a comparison of these three scraping alternatives.

Analyzing a web page

To understand how a web page is structured, we can try examining the source code. In most web browsers, the source code of a web page can be viewed by right-clicking on the page and selecting the View page source option:

The data we are interested in is found in this part of the HTML:

<table>
<tr id="places_national_flag__row"><td class="w2p_fl"><label for="places_national_flag" id="places_national_flag__label">National Flag: </label></td><td class="w2p_fw"><img src="/places/static/images/flags/gb.png" /></td><td class="w2p_fc"></td></tr>
...
<tr id="places_neighbours__row"><td class="w2p_fl"><label for="places_neighbours" id="places_neighbours__label">Neighbours: </label></td><td class="w2p_fw"><div><a href="/iso/IE">IE </a></div></td><td class="w2p_fc"></td></tr></table>

This lack of whitespace and formatting...

Three approaches to scrape a web page

Now that we understand the structure of this web page we will investigate three different approaches to scraping its data, firstly with regular expressions, then with the popular BeautifulSoup module, and finally with the powerful lxml module.

Regular expressions

If you are unfamiliar with regular expressions or need a reminder, there is a thorough overview available at https://docs.python.org/2/howto/regex.html.

To scrape the area using regular expressions, we will first try matching the contents of the <td> element, as follows:

>>> import re
>>> url = 'http://example.webscraping.com/view/UnitedKingdom-239'
>>> html = download(url)
>>> re.findall('<td class="w2p_fw">(.*?)</td>', html)
['<img src="/places/static/images/flags/gb.png" />',
  '244,820 square kilometres',
  '62,348,447',
  'GB',
  'United Kingdom',
  'London',
  '<a href="/continent/EU">EU</a>',
  '.uk',
  'GBP',
  'Pound...

Summary

In this chapter, we walked through a variety of ways to scrape data from a web page. Regular expressions can be useful for a one-off scrape or to avoid the overhead of parsing the entire web page, and BeautifulSoup provides a high-level interface while avoiding any difficult dependencies. However, in general, lxml will be the best choice because of its speed and extensive functionality, so we will use it in future examples.

In the next chapter we will introduce caching, which allows us to save web pages so that they only need be downloaded the first time a crawler is run.