Scrapy at a glance

Scrapy a is an application framework for crawling web sites and extracting structured data which can be used for a wide range of useful applications, like data mining, information processing or historical archival.

Even though Scrapy was originally designed for screen scraping (more precisely, web scraping), it can also be used to extract data using APIs (such as Amazon Associates Web Services) or as a general purpose web crawler.

The purpose of this document is to introduce you to the concepts behind Scrapy so you can get an idea of how it works and decide if Scrapy is what you need.

When you’re ready to start a project, you can start with the tutorial.

Pick a website

So you need to extract some information from a website, but the website doesn’t provide any API or mechanism to access that info from a computer program. Scrapy can help you extract that information. Let’s say we want to extract information about all torrent files added today in the mininova torrent site.

The list of all torrents added today can be found in this page:

Write a Spider to extract the Items

Now we’ll write a Spider which defines the start URL (, the rules for following links and the rules for extracting the data from pages.

If we take a look at that page content we’ll see that all torrent URLs are like where NUMBER is an integer. We’ll use that to construct the regular expression for the links to follow: /tor/\d+.

For extracting data we’ll use XPath to select the part of the document where the data is to be extracted. Let’s take one of those torrent pages:

And look at the page HTML source to construct the XPath to select the data we want to extract which is: torrent name, description and size.

By looking at the page HTML source we can see that the file name is contained inside a <h1> tag:


An XPath expression to extract the name could be:


And the description is contained inside a <div> tag with id="description":


<div id="description">
"HOME" - a documentary film by Yann Arthus-Bertrand
"We are living in exceptional times. Scientists tell us that we have 10 years to change the way we live, avert the depletion of natural resources and the catastrophic evolution of the Earth's climate.


An XPath expression to select the description could be:


Finally, the file size is contained in the second <p> tag inside the <div> tag with id=specifications:

<div id="specifications">

<a href="/cat/4">Movies</a> &gt; <a href="/sub/35">Documentary</a>

<strong>Total size:</strong>

An XPath expression to select the description could be:


For more information about XPath see the XPath reference.

Finally, here’s the spider code:

class MininovaSpider(CrawlSpider):

    name = ''
    allowed_domains = ['']
    start_urls = ['']
    rules = [Rule(SgmlLinkExtractor(allow=['/tor/\d+']), 'parse_torrent')]

    def parse_torrent(self, response):
        x = HtmlXPathSelector(response)

        torrent = TorrentItem()
        torrent['url'] = response.url
        torrent['name'] ="//h1/text()").extract()
        torrent['description'] ="//div[@id='description']").extract()
        torrent['size'] ="//div[@id='info-left']/p[2]/text()[2]").extract()
        return torrent

For brevity sake, we intentionally left out the import statements and the Torrent class definition (which is included some paragraphs above).

Write a pipeline to store the items extracted

Now let’s write an Item Pipeline that serializes and stores the extracted item into a file using pickle:

import pickle

class StoreItemPipeline(object):
    def process_item(self, spider, item):
        torrent_id = item['url'].split('/')[-1]
        f = open("torrent-%s.pickle" % torrent_id, "w")
        pickle.dump(item, f)

What else?

You’ve seen how to extract and store items from a website using Scrapy, but this is just the surface. Scrapy provides a lot of powerful features for making scraping easy and efficient, such as:

What’s next?

The next obvious steps are for you to download Scrapy, read the tutorial and join the community. Thanks for your interest!