Today, we are going to learn about Scrapy; what Scrapy is overall, Scrapy versus other Python-based web scraping tools, why you should use it and when it makes sense to use some other tools, pros and cons of Scrapy, and that would be it. So let’s begin!
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Scrapy, overall, is a web crawling framework written in Python. One of its main advantages is that it’s built on top of Twisted, an asynchronous networking framework, which in other words means that it’s: a) really efficient, and b) Scrapy is an asynchronous framework. So, to illustrate why this is a great feature, I’ll use, for those of you that don’t know what an asynchronous scraping framework means, I’ll use some enlightening example. So, imagine you have to call hundred different people by phone numbers. Well, normally you’d do it by sitting down and then dialing the first number and then patiently waiting for the response on the other end. In an asynchronous world, you can pretty much dial in first 20 or 50 phone numbers and then only process those calls once the other person on the other end picks up the phone. Hopefully, now it makes sense.
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Scrapy is supported under or uses Python 2.7 and Python 3.3. So you can pretty much, depending on your version of Python, you are pretty much good to go. So Python 2.6, important thing to note, support was dropped starting at Scrapy 0.20. So just bear that in mind, and Python 3 support was added in Scrapy 1.1
Scrapy, in some ways, is similar to Django. So those of you that use or have used, previously, Django will definitely benefit. Now let’s talk more about other Python-based scraping tools. Bear in mind that these are old-specialized libraries with very focused functionality and they don’t claim or they are not really a complete web scraping solution like Scrapy is.
The first two, urllib2 and then Requests are modules for reading or opening web pages, so HTTP models. The other two are Beautiful Soup and then lxml. These are for, aka, the fun part of the scraping jobs. Or really for extracting data points from those pages that are loaded with urllib2 and then Requests.
urllib2’s biggest advantage is that it’s included in the Python standard library so it’s batteries-included and as long as you have Python installed, you are good to go. In the past, it was more popular but since then another tool replaced it. And that tool, believe it or not, is called Requests.
The docs or documentations are superb for Requests. I think it’s even the most popular module for Python, period. And if you haven’t already, once again the docs are just amazing, so if you haven’t already, just give it a read.
Requests, unfortunately, doesn’t come pre-installed with Python, so you’ll have to install it. I personally use it for quick and dirty scraping jobs, and both urllib2 and Requests are supported with Python 2 and Python 3.
BeautifulSoup is used for extracting data points from the pages that are loaded. Beautiful Soup is quite robust and it handles nicely malformed markup. So, in other words, if you have a page that is not getting validated as a proper HTML but you know for a fact that it’s a page and that it’s an HTML specifically page, then you should give it a try scraping data from it with Beautiful Soup. So actually the name came from the expression ‘tag soup’ which is used to describe a really invalid markup. Beautiful Soup creates a parse tree that can be used to extract data from HTML.
The official docs are comprehensive, easy to read and with lots of examples. So they are really, just like with Requests, they are really, beginner-friendly. And just like the other tools for scraping, Beautiful Soup also comes with Python 2 and Python 3.
lxml is just similar to Beautiful Soup. It handles or it’s used for scraping data. It’s the most feature-rich Python library for processing both XML and HTML. It’s also really fast and memory efficient. A fun fact is that Scrapy selectors are built over lxml and for example, Beautiful Soup also supports it as a parser. Just like with Requests, I personally use lxml in pair with Requests of course, for again as previously mentioned, quick and dirty jobs.
The official documentation of lxml is not that beginner-friendly to be honest. And so if you haven’t already used a similar tool in the past, use examples from blogs or other sites. It’ll probably make a bit more sense than the official way of reading.
Selenium official docs are, once again, great and easy to grasp. And you can probably give it a read even if you are a complete beginner. And in two hours you will, pretty much, figure all out.
Scrapy Pros and Cons
Bear in mind that, from my testing, for example, Scraping thousand pages from Wikipedia was 20 times faster, believe it or not, in Scrapy than in Selenium. Also, on top of that, Scrapy consumed a lot less memory and CPU usage was a lot lower with Scrapy than with Selenium. So back to the Scrapy main pros, and when using Scrapy, of course, first and foremost it’s asynchronous but if you are building something robust and want to make it as efficient as possible with lots of flexibility and a bunch of functions, then you should definitely use it.
One case example when using some other tools, like the previously mentioned tools, kind of makes sense is if you had a project where you need to load the Home Page or something like that or your favorite, let’s say, restaurant and check if they are having your favorite dish on the menu. And then for this type of cases, you should not use Scrapy because, to be honest, it would be overkill. Some of the drawbacks of Scrapy is that, since it’s really a full-fledged framework, it’s not that beginner-friendly and the learning curve is a little steeper than some other tools. Also, installing Scrapy is a tricky process, especially with Windows. But bear in mind that you have a lot of resources online for this and this pretty much means that you have, I’m not even kidding, probably thousand blog posts about installing Scrapy on your specific operating system.
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