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Python Web Scraping
Python Web Scraping

Python Web Scraping: Successfully scrape data from any website with the power of Python

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Python Web Scraping

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:

Analyzing a web page

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...

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&apos...

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.

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Description

The Internet contains the most useful set of data ever assembled, largely publicly accessible for free. However, this data is not easily reusable. It is embedded within the structure and style of websites and needs to be carefully extracted to be useful. Web scraping is becoming increasingly useful as a means to easily gather and make sense of the plethora of information available online. Using a simple language like Python, you can crawl the information out of complex websites using simple programming. This book is the ultimate guide to using Python to scrape data from websites. In the early chapters it covers how to extract data from static web pages and how to use caching to manage the load on servers. After the basics we'll get our hands dirty with building a more sophisticated crawler with threads and more advanced topics. Learn step-by-step how to use Ajax URLs, employ the Firebug extension for monitoring, and indirectly scrape data. Discover more scraping nitty-gritties such as using the browser renderer, managing cookies, how to submit forms to extract data from complex websites protected by CAPTCHA, and so on. The book wraps up with how to create high-level scrapers with Scrapy libraries and implement what has been learned to real websites.

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 threaded crawler to process web pages in parallel
  • Follow links to crawl a website
  • Download cache to reduce bandwidth
  • Use multiple threads and processes to scrape faster
  • Learn how to parse JavaScriptdependent websites
  • Interact with forms and sessions
  • Solve CAPTCHAs on protected web pages
  • Discover how to track the state of a crawl

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Publication date : Oct 28, 2015
Length: 174 pages
Edition : 1st
Language : English
ISBN-13 : 9781782164364
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Product Details

Publication date : Oct 28, 2015
Length: 174 pages
Edition : 1st
Language : English
ISBN-13 : 9781782164364
Category :
Languages :
Concepts :
Tools :

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Table of Contents

10 Chapters
1. Introduction to Web Scraping Chevron down icon Chevron up icon
2. Scraping the Data Chevron down icon Chevron up icon
3. Caching Downloads Chevron down icon Chevron up icon
4. Concurrent Downloading Chevron down icon Chevron up icon
5. Dynamic Content Chevron down icon Chevron up icon
6. Interacting with Forms Chevron down icon Chevron up icon
7. Solving CAPTCHA Chevron down icon Chevron up icon
8. Scrapy Chevron down icon Chevron up icon
9. Overview Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

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Tim Crothers Dec 04, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Hands down the best resource I've found for practical examples of how to write web scrapers in Python. The author's style is very easy to read and very practical focused. He also clearly knows the subject inside and out and does a great job of not only showing you actual working code to do everything but also covers multiple approaches for different situations as well as key pitfalls to avoid.
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Bob Mar 23, 2017
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I am a python guy and wanted to get into web scraping. This book was perfect for me. Very well written and great examples. Hope to see more from this author in the future.
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Jean Marc QUERE Dec 04, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
If you need to collect (on a regular basis) data on a (or more) website(s), this book is for you. It explains how to load, extract, transform and store data in a convenient (and respectful) way. You will learn and practice this with Python. Prior to this, I had to wrote compiled programs and any change was boring. Now with Python, I can easily change my scripts without having to rebuild each program. The chapters about CAPTCHA and Scrapy were very useful for me... but don't tell it : I like to think I can do things that others can't :-)
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Austin Miller Mar 26, 2018
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Great resource!
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Julian Cook Dec 29, 2015
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This is a good up-to-date book on grabbing data from web sources..especially for python users who are not professional developers. I have found that any time you collect data, you normally end up either needing to auto-refresh the data, or you end up needing meta-data about the raw data. He has a good example, which is country statistics - something that you may need to 'normalize' other statistical data you are already working with.Typically python libs dealing with url fetching have very simple examples of the parser.parse(<html>"hello world"</html>) type, so you have no idea what to do with a real web page, like a 'needs login' page.The author, to his credit, does not tell you to download a 'magic' python library.In these cases he gives a thorough walk-through on how to research the structure or scripting of the page and then go about fetching it via python. Most of the book is in fact devoted to analysis, rather than action.The only parts that didn't seem particularly useful to me, were the chapters on creating crawlers and spiders. I don't see myself doing any of that and I don't see why an amateur or professional would use them. A professional would probably use Elasticsearch for instance.Other than that the book will probably be useful for a long time.
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