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Python Web Scraping
Python Web Scraping

Python Web Scraping: Hands-on data scraping and crawling using PyQT, Selnium, HTML and Python , Second Edition

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Python Web Scraping

Scraping the Data

In the previous chapter, we built a crawler which 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 browser tools 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.

In this chapter, we will cover the following topics:

  • Analyzing a web page
  • Approaches to scrape a web page
  • Using the console
  • xpath selectors
  • Scraping results
...

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:

For our example website, the data we are interested in is found on the country pages. Take a look at page source (via browser menu or right click browser menu). In the source for the example page for the United Kingdom (http://example.webscraping.com/view/United-Kingdom-239) you will find a table containing the country data (you can use search to find this in the page source code):

<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...

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, first 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/3/howto/regex.html. Even if you use regular expressions (or regex) with another programming language, I recommend stepping through it for a refresher on regex with Python.

Because each chapter might build or use parts of previous chapters, we recommend setting up your file structure similar to that in the book repository. All code can then...

CSS selectors and your Browser Console

Like the notation we used to extract using cssselect, CSS selectors are patterns used for selecting HTML elements. Here are some examples of common selectors you should know:

Select any tag: * 
Select by tag <a>: a
Select by class of "link": .link
Select by tag <a> with class "link": a.link
Select by tag <a> with ID "home": a#home
Select by child <span> of tag <a>: a > span
Select by descendant <span> of tag <a>: a span
Select by tag <a> with attribute title of "Home": a[title=Home]

The cssselect library implements most CSS3 selectors, and details on unsupported features (primarily browser interactions) are available at https://cssselect.readthedocs.io/en/latest/#supported-selectors.

The CSS3 specification was produced by the W3C and is available for viewing at http://www.w3.org/TR/2011...

XPath Selectors

There are times when using CSS selectors will not work. This is especially the case with very broken HTML or improperly formatted elements. Despite the best efforts of libraries like BeautifulSoup and lxml to properly parse and clean up the code; it will not always work - and in these cases, XPath can help you build very specific selectors based on hierarchical relationships of elements on the page.

XPath is a way of describing relationships as an hierarchy in XML documents. Because HTML is formed using XML elements, we can also use XPath to navigate and select elements from an HTML document.

To read more about XPath, check out the Mozilla developer documentation: https://developer.mozilla.org/en-US/docs/Web/XPath.

XPath follows some basic syntax rules and has some similarities with CSS selectors. Take a look at the following chart for some quick references between the two.

Selector description...

LXML and Family Trees

lxml also has the ability to traverse family trees within the HTML page. What is a family tree? When you used your browser's developer tools to investigate the elements on the page and you were able to expand or retract them, you were observing family relationships in the HTML. Every element on a web page can have parents, siblings and children. These relationships can help us more easily traverse the page.

For example, if I want to find all the elements at the same node depth level on the page, I would be looking for their siblings. Or maybe I want every element that is a child of a particular element on the page. lxml allows us to use many of these relationships with simple Python code.

As an example, let's investigate all children of the table element on the example page:

>>> table = tree.xpath('//table')[0]
>>> table.getchildren()
[<Element tr at...

Comparing performance

To help evaluate the trade-offs between the three scraping approaches described in the section, Three approaches to scrape a web page, it would be helpful to compare their relative efficiency. Typically, a scraper would extract multiple fields from a web page. So, for a more realistic comparison, we will implement extended versions of each scraper which extract all the available data from a country's web page. To get started, we need to return to our browser to check the format of the other country features, as shown here:

By using our browser's inspect capabilities, we can see each table row has an ID starting with places_ and ending with __row. The country data is contained within these rows in the same format as the area example. Here are implementations that use this information to extract all of the available country data:

FIELDS = ('area', 'population',...

Scraping results

Now that we have complete implementations for each scraper, we will test their relative performance with this snippet. The imports in the code expect your directory structure to be similar to the book's repository, so please adjust as necessary:

import time
import re
from chp2.all_scrapers import re_scraper, bs_scraper,
lxml_scraper, lxml_xpath_scraper
from chp1.advanced_link_crawler import download

NUM_ITERATIONS = 1000 # number of times to test each scraper
html = download('http://example.webscraping.com/places/view/United-Kingdom-239')

scrapers = [
('Regular expressions', re_scraper),
('BeautifulSoup', bs_scraper),
('Lxml', lxml_scraper),
('Xpath', lxml_xpath_scraper)]

for name, scraper in scrapers:
# record start time of scrape
start = time.time()
for i in range(NUM_ITERATIONS):
if scraper == re_scraper:
re...

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.

We also learned how to inspect HTML pages using browser tools and the console and define CSS selectors and XPath selectors to match and extract content from the downloaded pages.

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

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Key benefits

  • A hands-on guide to web scraping using Python with solutions to real-world problems
  • Create a number of different web scrapers in Python to extract information
  • This book includes practical examples on using the popular and well-maintained libraries in Python for your web scraping needs

Description

The Internet contains the most useful set of data ever assembled, most of which is publicly accessible for free. However, this data is not easily usable. It is embedded within the structure and style of websites and needs to be carefully extracted. Web scraping is becoming increasingly useful as a means to gather and make sense of the wealth of information available online. This book is the ultimate guide to using the latest features of Python 3.x to scrape data from websites. In the early chapters, you'll see how to extract data from static web pages. You'll learn to use caching with databases and files to save time and manage the load on servers. After covering the basics, you'll get hands-on practice building a more sophisticated crawler using browsers, crawlers, and concurrent scrapers. You'll determine when and how to scrape data from a JavaScript-dependent website using PyQt and Selenium. You'll get a better understanding of how to submit forms on complex websites protected by CAPTCHA. You'll find out how to automate these actions with Python packages such as mechanize. You'll also learn how to create class-based scrapers with Scrapy libraries and implement your learning on real websites. By the end of the book, you will have explored testing websites with scrapers, remote scraping, best practices, working with images, and many other relevant topics.

Who is this book for?

This book is aimed at developers who want to use web scraping for legitimate purposes. Prior programming experience with Python would be useful but not essential. Anyone with general knowledge of programming languages should be able to pick up the book and understand the principals involved.

What you will learn

  • • Extract data from web pages with simple Python programming
  • • Build a concurrent crawler to process web pages in parallel
  • • Follow links to crawl a website
  • • Extract features from the HTML
  • • Cache downloaded HTML for reuse
  • • Compare concurrent models to determine the fastest crawler
  • • Find out how to parse JavaScript-dependent websites
  • • Interact with forms and sessions
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Table of Contents

9 Chapters
Introduction to Web Scraping Chevron down icon Chevron up icon
Scraping the Data Chevron down icon Chevron up icon
Caching Downloads Chevron down icon Chevron up icon
Concurrent Downloading Chevron down icon Chevron up icon
Dynamic Content Chevron down icon Chevron up icon
Interacting with Forms Chevron down icon Chevron up icon
Solving CAPTCHA Chevron down icon Chevron up icon
Scrapy Chevron down icon Chevron up icon
Putting It All Together Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
(2 Ratings)
5 star 0%
4 star 50%
3 star 0%
2 star 50%
1 star 0%
Gerry Aug 26, 2017
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Finally a book that covers more than just the basics of webscraping. Packt needs better proof readers though. Language errors.
Amazon Verified review Amazon
Anonymous Feb 17, 2018
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
I would not recommend this book for any beginners in Python Web Scraping. Why? The website example they use in the book HAS NOT BEEN maintained and the code used in the book to reference the example website DOES NOT MATCH. I also found multiple complaints on the Internet from others. You will be so frustrated figuring out if you typed the code wrong, where in fact, the website links of the actual site don't match what's typed in the book. I'm glad I have some prior programming experience where I can fix some of the issues I experienced on the fly, but this takes additional time and testing. Overall, the book does go in depth and I think will be good for those with prior Python Web Scraping experience.
Amazon Verified review Amazon
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