# Scrapy Tutorial

In this tutorial, we’ll assume that Scrapy is already installed on your system. If that’s not the case, see Installation guide.

We are going to use Open directory project (dmoz) as our example domain to scrape.

This tutorial will walk you through these tasks:

1. Creating a new Scrapy project
2. Defining the Items you will extract
3. Writing a spider to crawl a site and extract Items
4. Writing an 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 Learn Python The Hard Way. 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 you start scraping, you will have to set up a new Scrapy project. Enter a directory where you’d like to store your code and run:

    scrapy startproject tutorial

This will create a `tutorial` directory with the following contents:

    tutorial/
        scrapy.cfg            # deploy configuration file

        tutorial/             # project's Python module, you'll import your code from here
            __init__.py

            items.py          # project items file

            pipelines.py      # project pipelines file

            settings.py       # project settings file

            spiders/          # a directory where you'll later put your spiders
                __init__.py
                ...

## Defining our Item

Items are containers that will be loaded with the scraped data; they work like simple Python dicts. While you can use plain Python dicts with Scrapy, Items provide additional protection against populating undeclared fields, preventing typos. They can also be used with Item Loaders, a mechanism with helpers to conveniently populate Items.

They are declared by creating a `scrapy.Item` class and defining its attributes as `scrapy.Field` objects, much like in an ORM (don’t worry if you’re not familiar with ORMs, you will see that this is an easy task).

We begin by modeling the item that we will use to hold the site’s 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. To do that, we edit `items.py`, found in the `tutorial` directory. Our Item class looks like this:

    import scrapy

    class DmozItem(scrapy.Item):
        title = scrapy.Field()
        link = scrapy.Field()
        desc = scrapy.Field()

This may seem complicated at first, but defining the item allows you to use other handy components of Scrapy that need to know what does your item look like.

## Our first Spider

Spiders are classes that you define and Scrapy uses 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 pages to extract items.

To create a Spider, you must subclass `scrapy.Spider` and define some attributes:

- `name`: identifies the Spider. It must be unique, that is, you can’t set the same name for different Spiders.

- `start_urls`: a list of URLs where the Spider will begin to crawl from. The first pages downloaded will be those listed here. The subsequent URLs will be generated successively from data contained in the start URLs.

- `parse()`: a method of the spider, which will be called with the downloaded `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 `parse()` method is in charge of processing the response and returning scraped data (as `Item` objects) and more URLs to follow (as `Request` objects).

This is the code for our first Spider; save it in a file named `dmoz_spider.py` under the `tutorial/spiders` directory:

    import scrapy

    class DmozSpider(scrapy.Spider):
        name = "dmoz"
        allowed_domains = ["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] + '.html'
            with open(filename, 'wb') as f:
                f.write(response.body)

### Crawling

To put our spider to work, go to the project’s top level directory and run:

    scrapy crawl dmoz

This command runs the spider with name `dmoz` that we’ve just added, that will send some requests for the `dmoz.org` domain. You will get an output similar to this:

    2014-01-23 18:13:07-0400 [scrapy] INFO: Scrapy started (bot: tutorial)
    2014-01-23 18:13:07-0400 [scrapy] INFO: Optional features available: ...
    2014-01-23 18:13:07-0400 [scrapy] INFO: Overridden settings: {}
    2014-01-23 18:13:07-0400 [scrapy] INFO: Enabled extensions: ...
    2014-01-23 18:13:07-0400 [scrapy] INFO: Enabled downloader middlewares: ...
    2014-01-23 18:13:07-0400 [scrapy] INFO: Enabled spider middlewares: ...
    2014-01-23 18:13:07-0400 [scrapy] INFO: Enabled item pipelines: ...
    2014-01-23 18:13:07-0400 [scrapy] INFO: Spider opened
    2014-01-23 18:13:08-0400 [scrapy] DEBUG: Crawled (200) <GET http://www.dmoz.org/Computers/Programming/Languages/Python/Resources/> (referer: None)
    2014-01-23 18:13:09-0400 [scrapy] DEBUG: Crawled (200) <GET http://www.dmoz.org/Computers/Programming/Languages/Python/Books/> (referer: None)
    2014-01-23 18:13:09-0400 [scrapy] INFO: Closing spider (finished)

Note

At the end 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 `(referer: None)`.

Now, check the files in the current directory. You should notice two new files have been created: Books.html and Resources.html, with the content for the respective URLs, as our `parse` method instructs.

#### What just happened under the hood?

Scrapy creates `scrapy.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 `scrapy.http.Response` objects are returned and then fed back to the spider, through the `parse()` method.

### Extracting Items

#### Introduction to Selectors

There are several ways to extract data from web pages. Scrapy uses a mechanism based on XPath or CSS expressions called Scrapy Selectors. For more information about selectors and other extraction mechanisms see the Selectors documentation.

Here are some examples of XPath expressions and their meanings:

- `/html/head/title`: selects the `<title>` element, inside the `<head>` element of an 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 expressions are indeed much more powerful. To learn more about XPath, we recommend this tutorial to learn XPath through examples, and this tutorial to learn “how to think in XPath”.

Note

CSS vs XPath: you can go a long way extracting data from web pages using only CSS selectors. However, XPath offers more power because besides navigating the structure, it can also look at the content: you’re able to select things like: the link that contains the text ‘Next Page’. Because of this, we encourage you to learn about XPath even if you already know how to construct CSS selectors.

For working with CSS and XPath expressions, Scrapy provides `Selector` class and convenient shortcuts to avoid instantiating selectors yourself every time you need to select something from a response.

You can see selectors as objects that represent nodes in the document structure. So, the first instantiated selectors are associated with the root node, or the entire document.

Selectors have four basic methods (click on the method to see the complete API documentation):

- `xpath()`: returns a list of selectors, each of which represents the nodes selected by the xpath expression given as argument.
- `css()`: returns a list of selectors, each of which represents the nodes selected by the CSS expression given as argument.
- `extract()`: returns a unicode string with the selected data.
- `re()`: returns a list of 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 Scrapy 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:

    scrapy shell "http://www.dmoz.org/Computers/Programming/Languages/Python/Books/"

Note

Remember to always enclose urls in quotes when running Scrapy shell from command-line, otherwise urls containing arguments (ie. `&` character) will not work.

This is what the shell looks like:

    [ ... Scrapy log here ... ]

    2014-01-23 17:11:42-0400 [scrapy] DEBUG: Crawled (200) <GET http://www.dmoz.org/Computers/Programming/Languages/Python/Books/> (referer: None)
    [s] Available Scrapy objects:
    [s]   crawler    <scrapy.crawler.Crawler object at 0x3636b50>
    [s]   item       {}
    [s]   request    <GET http://www.dmoz.org/Computers/Programming/Languages/Python/Books/>
    [s]   response   <200 http://www.dmoz.org/Computers/Programming/Languages/Python/Books/>
    [s]   settings   <scrapy.settings.Settings object at 0x3fadc50>
    [s]   spider     <Spider 'default' at 0x3cebf50>
    [s] Useful shortcuts:
    [s]   shelp()           Shell help (print this help)
    [s]   fetch(req_or_url) Fetch request (or URL) and update local objects
    [s]   view(response)    View response in a browser

    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 type `response.headers` to see its headers.

More importantly `response` has a `selector` attribute which is an instance of `Selector` class, instantiated with this particular `response`. You can run queries on `response` by calling `response.selector.xpath()` or `response.selector.css()`. There are also some convenience shortcuts like `response.xpath()` or `response.css()` which map directly to `response.selector.xpath()` and `response.selector.css()`.

So let’s try it:

    In [1]: response.xpath('//title')
    Out[1]: [<Selector xpath='//title' data=u'<title>Open Directory - Computers: Progr'>]

    In [2]: response.xpath('//title').extract()
    Out[2]: [u'<title>Open Directory - Computers: Programming: Languages: Python: Books</title>']

    In [3]: response.xpath('//title/text()')
    Out[3]: [<Selector xpath='//title/text()' data=u'Open Directory - Computers: Programming:'>]

    In [4]: response.xpath('//title/text()').extract()
    Out[4]: [u'Open Directory - Computers: Programming: Languages: Python: Books']

    In [5]: response.xpath('//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 it easier, you can use Firefox Developer Tools or some Firefox extensions like Firebug. For more information see Using Firebug for scraping and Using Firefox for scraping.

After inspecting the page source, you’ll find that the web site’s information is inside a `<ul>` element, in fact the second `<ul>` element.

So we can select each `<li>` element belonging to the site’s list with this code:

    response.xpath('//ul/li')

And from them, the site’s descriptions:

    response.xpath('//ul/li/text()').extract()

The site’s titles:

    response.xpath('//ul/li/a/text()').extract()

And the site’s links:

    response.xpath('//ul/li/a/@href').extract()

As we’ve said before, each `.xpath()` call returns a list of selectors, so we can concatenate further `.xpath()` calls to dig deeper into a node. We are going to use that property here, so:

    for sel in response.xpath('//ul/li'):
        title = sel.xpath('a/text()').extract()
        link = sel.xpath('a/@href').extract()
        desc = sel.xpath('text()').extract()
        print title, link, desc

Note

For a more detailed description of using nested selectors, see Nesting selectors and Working with relative XPaths in the Selectors documentation

Let’s add this code to our spider:

    import scrapy

    class DmozSpider(scrapy.Spider):
        name = "dmoz"
        allowed_domains = ["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):
            for sel in response.xpath('//ul/li'):
                title = sel.xpath('a/text()').extract()
                link = sel.xpath('a/@href').extract()
                desc = sel.xpath('text()').extract()
                print title, link, desc

Now try crawling dmoz.org again and you’ll see sites being printed in your output. Run:

    scrapy crawl dmoz

### Using our item

`Item` objects are custom Python dicts; you can access the values of their fields (attributes of the class we defined earlier) using the standard dict syntax like:

    >>> item = DmozItem()
    >>> item['title'] = 'Example title'
    >>> item['title']
    'Example title'

So, in order to return the data we’ve scraped so far, the final code for our Spider would be like this:

    import scrapy

    from tutorial.items import DmozItem

    class DmozSpider(scrapy.Spider):
        name = "dmoz"
        allowed_domains = ["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):
            for sel in response.xpath('//ul/li'):
                item = DmozItem()
                item['title'] = sel.xpath('a/text()').extract()
                item['link'] = sel.xpath('a/@href').extract()
                item['desc'] = sel.xpath('text()').extract()
                yield item

Note

You can find a fully-functional variant of this spider in the dirbot project available at https://github.com/scrapy/dirbot

Now crawling dmoz.org yields `DmozItem` objects:

    [scrapy] DEBUG: Scraped from <200 http://www.dmoz.org/Computers/Programming/Languages/Python/Books/>
         {'desc': [u' - By David Mertz; Addison Wesley. Book in progress, full text, ASCII format. Asks for feedback. [author website, Gnosis Software, Inc.\n],
          'link': [u'http://gnosis.cx/TPiP/'],
          'title': [u'Text Processing in Python']}
    [scrapy] DEBUG: Scraped from <200 http://www.dmoz.org/Computers/Programming/Languages/Python/Books/>
         {'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'],
          'link': [u'http://www.informit.com/store/product.aspx?isbn=0130211192'],
          'title': [u'XML Processing with Python']}

## Following links

Let’s say, instead of just scraping the stuff in Books and Resources pages, you want everything that is under the Python directory.

Now that you know how to extract data from a page, why not extract the links for the pages you are interested, follow them and then extract the data you want for all of them?

Here is a modification to our spider that does just that:

    import scrapy

    from tutorial.items import DmozItem

    class DmozSpider(scrapy.Spider):
        name = "dmoz"
        allowed_domains = ["dmoz.org"]
        start_urls = [
            "http://www.dmoz.org/Computers/Programming/Languages/Python/",
        ]

        def parse(self, response):
            for href in response.css("ul.directory.dir-col > li > a::attr('href')"):
                url = response.urljoin(href.extract())
                yield scrapy.Request(url, callback=self.parse_dir_contents)

        def parse_dir_contents(self, response):
            for sel in response.xpath('//ul/li'):
                item = DmozItem()
                item['title'] = sel.xpath('a/text()').extract()
                item['link'] = sel.xpath('a/@href').extract()
                item['desc'] = sel.xpath('text()').extract()
                yield item

Now the parse() method only extract the interesting links from the page, builds a full absolute URL using the response.urljoin method (since the links can be relative) and yields new requests to be sent later, registering as callback the method parse_dir_contents() that will ultimately scrape the data we want.

What you see here is the Scrapy’s mechanism of following links: when you yield a Request in a callback method, Scrapy will schedule that request to be sent and register a callback method to be executed when that request finishes.

Using this, you can build complex crawlers that follow links according to rules you define, and extract different kinds of data depending on the page it’s visiting.

A common pattern is a callback method that extract some items, looks for a link to follow to the next page and then yields a Request with the same callback for it:

    def parse_articles_follow_next_page(self, response):
        for article in response.xpath("//article"):
            item = ArticleItem()

            ... extract article data here

            yield item

        next_page = response.css("ul.navigation > li.next-page > a::attr('href')")
        if next_page:
            url = response.urljoin(next_page[0].extract())
            yield Request(url, self.parse_articles_follow_next_page)

This creates a sort of loop, following all the links to the next page until it doesn’t find one – handy for crawling blogs, forums and other sites with pagination.

Another common pattern is to build an item with data from more than one page, using a trick to pass additional data to the callbacks.

Note

As an example spider that leverages this mechanism, check out the `CrawlSpider` class for a generic spider that implements a small rules engine that you can use to write your crawlers on top of it.

## Storing the scraped data

The simplest way to store the scraped data is by using Feed exports, with the following command:

    scrapy crawl dmoz -o items.json

That will generate an `items.json` file containing all scraped items, serialized in JSON.

In small projects (like the one in this tutorial), that should be enough. However, if you want to perform more complex things with the scraped items, you can write an Item Pipeline. As with Items, a placeholder file for Item Pipelines has been set up for you when the project is created, in `tutorial/pipelines.py`. Though you don’t need to implement any item pipelines if you just want to store the scraped items.

## Next steps

This tutorial covered only the basics of Scrapy, but there’s a lot of other features not mentioned here. Check the What else? section in Scrapy at a glance chapter for a quick overview of the most important ones.

Then, we recommend you continue by playing with an example project (see Examples), and then continue with the section Basic concepts.