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Scrapy Tutorial
In this tutorial, we'll assume that Scrapy is already installed in your system. If that's not the case see :ref:`intro-install`.
We are going to use Open directory project (dmoz) as our example domain to scrape.
This tutorial will walk you through through these tasks:
Creating a new Scrapy project
Defining the Items you will extract
Writing a :ref:`spider <topics-spiders>` to crawl a site and extract :ref:`Items <topics-items>`
Writing an :ref:`Item Pipeline <topics-item-pipeline>` to store the extracted Items
Scrapy is written in Python. If you're new to the language you might want to start by getting an idea of what the language is like, to get the most out of Scrapy. If you're already familiar with other languages, and want to learn Python quickly, we recommend Dive Into Python. If you're new to programming and want to start with Python, take a look at this list of Python resources for non-programmers.
Creating a project
Before start scraping, you will have set up a new Scrapy project. Enter a directory where you'd like to store your code and then run:
python scrapy-ctl.py startproject dmoz
This will create a dmoz directory with the following contents:
dmoz/
scrapy-ctl.py
dmoz/
__init__.py
items.py
pipelines.py
settings.py
spiders/
__init__.py
...
These are basically:
- scrapy-ctl.py: the project's control script.
- dmoz/: the project's python module, you'll later import your code from here.
- dmoz/items.py: the project's items file.
- dmoz/pipelines.py: the project's pipelines file.
- dmoz/settings.py: the project's settings file.
- dmoz/spiders/: a directory where you'll later put your spiders.
Defining our Item
Items are containers that will be loaded with the scraped data, they work like simple python dicts but they offer some additional features like providing default values.
They are declared by creating an :class:`scrapy.item.Item` class an defining its attributes as :class:`scrapy.item.Field` objects, like you will in an ORM (don't worry if you're not familiar with ORM's, you will see that this is an easy task).
We begin by modeling the item that we will use to hold the sites data obtained from dmoz.org, as we want to capture the name, url and description of the sites, we define fields for each of these three attributes. Our Item class looks like:
# Define here the models for your scraped items
from scrapy.item import Item, Field
class DmozItem(Item):
title = Field()
link = Field()
desc = Field()
This may seem complicated at first, but defining the item allows you to use other handy components of Scrapy that need to know how your item looks like.
Our first Spider
Spiders are user written classes to scrape information from a domain (or group of domains).
They define an initial list of URLs to download, how to follow links, and how to parse the contents of those pages to extract :ref:`items <topics-items>`.
To create a Spider, you must subclass :class:`scrapy.spider.BaseSpider`, and define the three main, mandatory, attributes:
:attr:`~scrapy.spider.BaseSpider.domain_name`: identifies the Spider. It must be unique, that is, you can't set the same domain name for different Spiders.
:attr:`~scrapy.spider.BaseSpider.start_urls`: is a list of URLs where the Spider will begin to crawl from. So, the first pages downloaded will be those listed here. The subsequent URLs will be generated successively from data contained in the start URLs.
:meth:`~scrapy.spider.BaseSpider.parse` is a method of the spider, which will be called with the downloaded :class:`~scrapy.http.Response` object of each start URL. The response is passed to the method as the first and only argument.
This method is responsible for parsing the response data and extracting scraped data (as scraped items) and more URLs to follow.
The :meth:`~scrapy.spider.BaseSpider.parse` method is in charge of processing the response and returning scraped data (as :class:`~scrapy.item.Item` objects) and more URLs to follow (as :class:`~scrapy.http.Request` objects).
This is the code for our first Spider, save it in a file named dmoz_spider.py under the dmoz/spiders directory:
from scrapy.spider import BaseSpider
class DmozSpider(BaseSpider):
domain_name = "dmoz.org"
start_urls = [
"http://www.dmoz.org/Computers/Programming/Languages/Python/Books/",
"http://www.dmoz.org/Computers/Programming/Languages/Python/Resources/"
]
def parse(self, response):
filename = response.url.split("/")[-2]
open(filename, 'wb').write(response.body)
SPIDER = DmozSpider()
Crawling
To put our spider to work, go to the project's top level directory and run:
python scrapy-ctl.py crawl dmoz.org
The crawl dmoz.org command runs the spider for the dmoz.org domain. You will get an output similar to this:
[-] Log opened. [dmoz] INFO: Enabled extensions: ... [dmoz] INFO: Enabled scheduler middlewares: ... [dmoz] INFO: Enabled downloader middlewares: ... [dmoz] INFO: Enabled spider middlewares: ... [dmoz] INFO: Enabled item pipelines: ... [dmoz.org] INFO: Domain opened [dmoz.org] DEBUG: Crawled <http://www.dmoz.org/Computers/Programming/Languages/Python/Resources/> from <None> [dmoz.org] DEBUG: Crawled <http://www.dmoz.org/Computers/Programming/Languages/Python/Books/> from <None> [dmoz.org] INFO: Domain closed (finished) [-] Main loop terminated.
Pay attention to the lines containing [dmoz.org], which corresponds to our spider (identified by the domain "dmoz.org"). You can see a log line for each URL defined in start_urls. Because these URLs are the starting ones, they have no referrers, which is shown at the end of the log line, where it says from <None>.
But more interesting, as our parse method instructs, two files have been created: Books and Resources, with the content of both URLs.
What just happened under the hood?
Scrapy creates :class:`scrapy.http.Request` objects for each URL in the start_urls attribute of the Spider, and assigns them the parse method of the spider as their callback function.
These Requests are scheduled, then executed, and a :class:`scrapy.http.Response` objects are returned and then fed back to the spider, through the :meth:`~scrapy.spider.BaseSpider.parse` method.
Extracting Items
Introduction to Selectors
There are several ways to extract data from web pages, Scrapy uses a mechanism based on XPath expressions called :ref:`XPath selectors <topics-selectors>`. For more information about selectors and other extraction mechanisms see the :ref:`XPath selectors documentation <topics-selectors>`.
Here are some examples of XPath expressions and their meanings:
- /html/head/title: selects the <title> element, inside the <head> element of a HTML document
- /html/head/title/text(): selects the text inside the aforementioned <title> element.
- //td: selects all the <td> elements
- //div[@class="mine"]: selects all div elements which contain an attribute class="mine"
These are just a couple of simple examples of what you can do with XPath, but XPath expression are indeed much more powerful. To learn more about XPath we recommend this XPath tutorial.
For working with XPaths, Scrapy provides a :class:`~scrapy.selector.XPathSelector` class, which comes in two flavours, :class:`~scrapy.selector.HtmlXPatSelector` (for HTML data) and :class:`~scrapy.selector.XmlXPathSelector` (for XML data). In order to use them you must instantiate the desired class with a :class:`~scrapy.http.Response` object.
You can see selectors as objects that represents nodes in the document structure. So, the first instantiated selectors are associated to the root node, or the entire document.
Selectors have three methods (click on the method to see the complete API documentation).
:meth:`~scrapy.selector.XPathSelector.x`: returns a list of selectors, each of them representing the nodes selected by the xpath expression given as argument.
- :meth:`~scrapy.selector.XPathSelector.extract`: returns a unicode string with
-
the data selected by the XPath selector.
:meth:`~scrapy.selector.XPathSelector.re`: returns a list unicode strings extracted by applying the regular expression given as argument.
Trying Selectors in the Shell
To illustrate the use of Selectors we're going to use the built-in :ref:`Scrapy shell <topics-shell>`, which also requires IPython (an extended Python console) installed on your system.
To start a shell you must go to the project's top level directory and run:
python scrapy-ctl.py shell http://www.dmoz.org/Computers/Programming/Languages/Python/Books/
This is what the shell looks like:
[-] Log opened. Welcome to Scrapy shell! Fetching <http://www.dmoz.org/Computers/Programming/Languages/Python/Books/>... ------------------------------------------------------------------------------ Available Scrapy variables: xxs: <class 'scrapy.selector.XmlXPathSelector'> url: http://www.dmoz.org/Computers/Programming/Languages/Python/Books/ spider: <class 'dmoz.spiders.dmoz.OpenDirectorySpider'> hxs: <class 'scrapy.selector.HtmlXPathSelector'> item: <class 'scrapy.item.Item'> response: <class 'scrapy.http.response.html.HtmlResponse'> Available commands: get [url]: Fetch a new URL or re-fetch current Request shelp: Prints this help. ------------------------------------------------------------------------------ Python 2.6.1 (r261:67515, Dec 7 2008, 08:27:41) Type "copyright", "credits" or "license" for more information. IPython 0.9.1 -- An enhanced Interactive Python. ? -> Introduction and overview of IPython's features. %quickref -> Quick reference. help -> Python's own help system. object? -> Details about 'object'. ?object also works, ?? prints more. In [1]:
After the shell loads, you will have the response fetched in a local response variable, so if you type response.body you will see the body of the response, or you can response.headers to see its headers.
The shell also instantiates two selectors, one for HTML (in the hxs variable) and one for XML (in the xxs variable)with this response. So let's try them:
In [1]: hxs.select('/html/head/title')
Out[1]: [<HtmlXPathSelector (title) xpath=/html/head/title>]
In [2]: hxs.select('/html/head/title').extract()
Out[2]: [u'<title>Open Directory - Computers: Programming: Languages: Python: Books</title>']
In [3]: hxs.select('/html/head/title/text()')
Out[3]: [<HtmlXPathSelector (text) xpath=/html/head/title/text()>]
In [4]: hxs.select('/html/head/title/text()').extract()
Out[4]: [u'Open Directory - Computers: Programming: Languages: Python: Books']
In [5]: hxs.select('/html/head/title/text()').re('(\w+):')
Out[5]: [u'Computers', u'Programming', u'Languages', u'Python']
Extracting the data
Now, let's try to extract some real information from those pages.
You could type response.body in the console, and inspect the source code to figure out the XPaths you need to use. However, inspecting the raw HTML code there could become a very tedious task. To make this an easier task, you can use some Firefox extensions like Firebug. For more information see :ref:`topics-firebug` and :ref:`topics-firefox`.
After inspecting the page source you'll find that the web sites information is inside a <ul> element, in fact the second <ul> element.
So we can select each <li> element belonging to the sites list with this code:
hxs.select('//ul[2]/li')
And from them, the sites descriptions:
hxs.select('//ul[2]/li/text()').extract()
The sites titles:
hxs.select('//ul[2]/li/a/text()').extract()
And the sites links:
hxs.select('//ul[2]/li/a/@href').extract()
As we said before, each select() call returns a list of selectors, so we can concatenate further select() calls to dig deeper into a node. We are going to use that property here, so:
sites = hxs.select('//ul[2]/li')
for site in sites:
title = site.select('a/text()').extract()
link = site.select('a/@href').extract()
desc = site.select('text()').extract()
print title, link, desc
Note
For a more detailed description of using nested selectors see :ref:`topics-selectors-nesting-selectors` and :ref:`topics-selectors-relative-xpaths` in :ref:`topics-selectors` documentation
Let's add this code to our spider:
from scrapy.spider import BaseSpider
from scrapy.selector import HtmlXPathSelector
class DmozSpider(BaseSpider):
domain_name = "dmoz.org"
start_urls = [
"http://www.dmoz.org/Computers/Programming/Languages/Python/Books/",
"http://www.dmoz.org/Computers/Programming/Languages/Python/Resources/"
]
def parse(self, response):
hxs = HtmlXPathSelector(response)
sites = hxs.select('//ul[2]/li')
for site in sites:
title = site.select('a/text()').extract()
link = site.select('a/@href').extract()
desc = site.select('text()').extract()
print title, link, desc
SPIDER = DmozSpider()
Now try crawling the dmoz.org domain again and you'll see sites being printed in your output, run:
python scrapy-ctl.py crawl dmoz.org
Using our item
:class:`~scrapy.item.Item` objects are custom python dict, you can access the values oftheir fields (attributes of the class we defined earlier) using the standard dict syntax like:
>>> item = DmozItem() >>> item['title'] = 'Example title' >>> item['title'] 'Example title'
Spiders are expected to return their scraped data inside :class:`~scrapy.item.Item` objects, so to actually return the data we've scraped so far, the code for our Spider should be like this:
from scrapy.spider import BaseSpider
from scrapy.selector import HtmlXPathSelector
from dmoz.items import DmozItem
class DmozSpider(BaseSpider):
domain_name = "dmoz.org"
start_urls = [
"http://www.dmoz.org/Computers/Programming/Languages/Python/Books/",
"http://www.dmoz.org/Computers/Programming/Languages/Python/Resources/"
]
def parse(self, response):
hxs = HtmlXPathSelector(response)
sites = hxs.select('//ul[2]/li')
items = []
for site in sites:
item = DmozItem()
item['title'] = site.select('a/text()').extract()
item['link'] = site.select('a/@href').extract()
item['desc'] = site.select('text()').extract()
items.append(item)
return items
SPIDER = DmozSpider()
Now doing a crawl on the dmoz.org domain yields DmozItem's:
[dmoz.org] DEBUG: Scraped DmozItem({'title': [u'Text Processing in Python'], 'link': [u'http://gnosis.cx/TPiP/'], 'desc': [u' - By David Mertz; Addison Wesley. Book in progress, full text, ASCII format. Asks for feedback. [author website, Gnosis Software, Inc.]\n']}) in <http://www.dmoz.org/Computers/Programming/Languages/Python/Books/>
[dmoz.org] DEBUG: Scraped DmozItem({'title': [u'XML Processing with Python'], 'link': [u'http://www.informit.com/store/product.aspx?isbn=0130211192'], 'desc': [u' - By Sean McGrath; Prentice Hall PTR, 2000, ISBN 0130211192, has CD-ROM. Methods to build XML applications fast, Python tutorial, DOM and SAX, new Pyxie open source XML processing library. [Prentice Hall PTR]\n']}) in <http://www.dmoz.org/Computers/Programming/Languages/Python/Books/>
Storing the data (using an Item Pipeline)
After an item has been scraped by a Spider, it is sent to the :ref:`Item Pipeline <topics-item-pipeline>`.
The Item Pipeline is a group of user written Python classes that implement a simple method. They receive an Item and perform an action over it (for example: validation, checking for duplicates, or storing it in a database), and then decide if the Item continues through the Pipeline or it's dropped and no longer processed.
In small projects (like the one on this tutorial) we will use only one Item Pipeline that just stores our Items.
As with Items, a Pipeline placeholder has been set up for you in the project creation step, it's in dmoz/pipelines.py and looks like this:
# Define your item pipelines here
class DmozPipeline(object):
def process_item(self, domain, item):
return item
We have to override the process_item method in order to store our Items somewhere.
Here's a simple pipeline for storing the scraped items into a CSV (comma separated values) file using the standard library csv module:
import csv
class CsvWriterPipeline(object):
def __init__(self):
self.csvwriter = csv.writer(open('items.csv', 'wb'))
def process_item(self, domain, item):
self.csvwriter.writerow([item['title'][0], item['link'][0], item['desc'][0]])
return item
Don't forget to enable the pipeline by adding it to the :setting:`ITEM_PIPELINES` setting in your settings.py, like this:
ITEM_PIPELINES = ['dmoz.pipelines.CsvWriterPipeline']
Finale
This tutorial covers only the basics of Scrapy, but there's a lot of other features not mentioned here. We recommend you continue reading the section :ref:`topics-index`.
