Scrapy 1.5 documentation

This documentation contains everything you need to know about Scrapy.

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First steps

Scrapy at a glance

Scrapy 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 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.

Walk-through of an example spider

In order to show you what Scrapy brings to the table, we’ll walk you through an example of a Scrapy Spider using the simplest way to run a spider.

Here’s the code for a spider that scrapes famous quotes from website, following the pagination:

import scrapy

class QuotesSpider(scrapy.Spider):
    name = "quotes"
    start_urls = [

    def parse(self, response):
        for quote in response.css('div.quote'):
            yield {
                'text': quote.css('span.text::text').extract_first(),
                'author': quote.xpath('span/small/text()').extract_first(),

        next_page = response.css(' a::attr("href")').extract_first()
        if next_page is not None:
            yield response.follow(next_page, self.parse)

Put this in a text file, name it to something like and run the spider using the runspider command:

scrapy runspider -o quotes.json

When this finishes you will have in the quotes.json file a list of the quotes in JSON format, containing text and author, looking like this (reformatted here for better readability):

    "author": "Jane Austen",
    "text": "\u201cThe person, be it gentleman or lady, who has not pleasure in a good novel, must be intolerably stupid.\u201d"
    "author": "Groucho Marx",
    "text": "\u201cOutside of a dog, a book is man's best friend. Inside of a dog it's too dark to read.\u201d"
    "author": "Steve Martin",
    "text": "\u201cA day without sunshine is like, you know, night.\u201d"
What just happened?

When you ran the command scrapy runspider, Scrapy looked for a Spider definition inside it and ran it through its crawler engine.

The crawl started by making requests to the URLs defined in the start_urls attribute (in this case, only the URL for quotes in humor category) and called the default callback method parse, passing the response object as an argument. In the parse callback, we loop through the quote elements using a CSS Selector, yield a Python dict with the extracted quote text and author, look for a link to the next page and schedule another request using the same parse method as callback.

Here you notice one of the main advantages about Scrapy: requests are scheduled and processed asynchronously. This means that Scrapy doesn’t need to wait for a request to be finished and processed, it can send another request or do other things in the meantime. This also means that other requests can keep going even if some request fails or an error happens while handling it.

While this enables you to do very fast crawls (sending multiple concurrent requests at the same time, in a fault-tolerant way) Scrapy also gives you control over the politeness of the crawl through a few settings. You can do things like setting a download delay between each request, limiting amount of concurrent requests per domain or per IP, and even using an auto-throttling extension that tries to figure out these automatically.


This is using feed exports to generate the JSON file, you can easily change the export format (XML or CSV, for example) or the storage backend (FTP or Amazon S3, for example). You can also write an item pipeline to store the items in a database.

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:

  • Built-in support for selecting and extracting data from HTML/XML sources using extended CSS selectors and XPath expressions, with helper methods to extract using regular expressions.
  • An interactive shell console (IPython aware) for trying out the CSS and XPath expressions to scrape data, very useful when writing or debugging your spiders.
  • Built-in support for generating feed exports in multiple formats (JSON, CSV, XML) and storing them in multiple backends (FTP, S3, local filesystem)
  • Robust encoding support and auto-detection, for dealing with foreign, non-standard and broken encoding declarations.
  • Strong extensibility support, allowing you to plug in your own functionality using signals and a well-defined API (middlewares, extensions, and pipelines).
  • Wide range of built-in extensions and middlewares for handling:
    • cookies and session handling
    • HTTP features like compression, authentication, caching
    • user-agent spoofing
    • robots.txt
    • crawl depth restriction
    • and more
  • A Telnet console for hooking into a Python console running inside your Scrapy process, to introspect and debug your crawler
  • Plus other goodies like reusable spiders to crawl sites from Sitemaps and XML/CSV feeds, a media pipeline for automatically downloading images (or any other media) associated with the scraped items, a caching DNS resolver, and much more!

What’s next?

The next steps for you are to install Scrapy, follow through the tutorial to learn how to create a full-blown Scrapy project and join the community. Thanks for your interest!

Installation guide

Installing Scrapy

Scrapy runs on Python 2.7 and Python 3.4 or above under CPython (default Python implementation) and PyPy (starting with PyPy 5.9).

If you’re using Anaconda or Miniconda, you can install the package from the conda-forge channel, which has up-to-date packages for Linux, Windows and OS X.

To install Scrapy using conda, run:

conda install -c conda-forge scrapy

Alternatively, if you’re already familiar with installation of Python packages, you can install Scrapy and its dependencies from PyPI with:

pip install Scrapy

Note that sometimes this may require solving compilation issues for some Scrapy dependencies depending on your operating system, so be sure to check the Platform specific installation notes.

We strongly recommend that you install Scrapy in a dedicated virtualenv, to avoid conflicting with your system packages.

For more detailed and platform specifics instructions, read on.

Things that are good to know

Scrapy is written in pure Python and depends on a few key Python packages (among others):

  • lxml, an efficient XML and HTML parser
  • parsel, an HTML/XML data extraction library written on top of lxml,
  • w3lib, a multi-purpose helper for dealing with URLs and web page encodings
  • twisted, an asynchronous networking framework
  • cryptography and pyOpenSSL, to deal with various network-level security needs

The minimal versions which Scrapy is tested against are:

  • Twisted 14.0
  • lxml 3.4
  • pyOpenSSL 0.14

Scrapy may work with older versions of these packages but it is not guaranteed it will continue working because it’s not being tested against them.

Some of these packages themselves depends on non-Python packages that might require additional installation steps depending on your platform. Please check platform-specific guides below.

In case of any trouble related to these dependencies, please refer to their respective installation instructions:

Platform specific installation notes


Though it’s possible to install Scrapy on Windows using pip, we recommend you to install Anaconda or Miniconda and use the package from the conda-forge channel, which will avoid most installation issues.

Once you’ve installed Anaconda or Miniconda, install Scrapy with:

conda install -c conda-forge scrapy
Ubuntu 14.04 or above

Scrapy is currently tested with recent-enough versions of lxml, twisted and pyOpenSSL, and is compatible with recent Ubuntu distributions. But it should support older versions of Ubuntu too, like Ubuntu 14.04, albeit with potential issues with TLS connections.

Don’t use the python-scrapy package provided by Ubuntu, they are typically too old and slow to catch up with latest Scrapy.

To install scrapy on Ubuntu (or Ubuntu-based) systems, you need to install these dependencies:

sudo apt-get install python-dev python-pip libxml2-dev libxslt1-dev zlib1g-dev libffi-dev libssl-dev
  • python-dev, zlib1g-dev, libxml2-dev and libxslt1-dev are required for lxml
  • libssl-dev and libffi-dev are required for cryptography

If you want to install scrapy on Python 3, you’ll also need Python 3 development headers:

sudo apt-get install python3 python3-dev

Inside a virtualenv, you can install Scrapy with pip after that:

pip install scrapy


The same non-Python dependencies can be used to install Scrapy in Debian Jessie (8.0) and above.

Mac OS X

Building Scrapy’s dependencies requires the presence of a C compiler and development headers. On OS X this is typically provided by Apple’s Xcode development tools. To install the Xcode command line tools open a terminal window and run:

xcode-select --install

There’s a known issue that prevents pip from updating system packages. This has to be addressed to successfully install Scrapy and its dependencies. Here are some proposed solutions:

  • (Recommended) Don’t use system python, install a new, updated version that doesn’t conflict with the rest of your system. Here’s how to do it using the homebrew package manager:

    • Install homebrew following the instructions in

    • Update your PATH variable to state that homebrew packages should be used before system packages (Change .bashrc to .zshrc accordantly if you’re using zsh as default shell):

      echo "export PATH=/usr/local/bin:/usr/local/sbin:$PATH" >> ~/.bashrc
    • Reload .bashrc to ensure the changes have taken place:

      source ~/.bashrc
    • Install python:

      brew install python
    • Latest versions of python have pip bundled with them so you won’t need to install it separately. If this is not the case, upgrade python:

      brew update; brew upgrade python
  • (Optional) Install Scrapy inside an isolated python environment.

    This method is a workaround for the above OS X issue, but it’s an overall good practice for managing dependencies and can complement the first method.

    virtualenv is a tool you can use to create virtual environments in python. We recommended reading a tutorial like to get started.

After any of these workarounds you should be able to install Scrapy:

pip install Scrapy

We recommend using the latest PyPy version. The version tested is 5.9.0. For PyPy3, only Linux installation was tested.

Most scrapy dependencides now have binary wheels for CPython, but not for PyPy. This means that these dependecies will be built during installation. On OS X, you are likely to face an issue with building Cryptography dependency, solution to this problem is described here, that is to brew install openssl and then export the flags that this command recommends (only needed when installing scrapy). Installing on Linux has no special issues besides installing build dependencies. Installing scrapy with PyPy on Windows is not tested.

You can check that scrapy is installed correctly by running scrapy bench. If this command gives errors such as TypeError: ... got 2 unexpected keyword arguments, this means that setuptools was unable to pick up one PyPy-specific dependency. To fix this issue, run pip install 'PyPyDispatcher>=2.1.0'.

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 scrape, a website that lists quotes from famous authors.

This tutorial will walk you through these tasks:

  1. Creating a new Scrapy project
  2. Writing a spider to crawl a site and extract data
  3. Exporting the scraped data using the command line
  4. Changing spider to recursively follow links
  5. Using spider arguments

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 reading through Dive Into Python 3. Alternatively, you can follow the Python Tutorial.

If you’re new to programming and want to start with Python, you may find useful the online book Learn Python The Hard Way. You can also 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:

    scrapy.cfg            # deploy configuration file

    tutorial/             # project's Python module, you'll import your code from here          # project items definition file    # project middlewares file      # project pipelines file       # project settings file

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

Our first Spider

Spiders are classes that you define and that Scrapy uses to scrape information from a website (or a group of websites). They must subclass scrapy.Spider and define the initial requests to make, optionally how to follow links in the pages, and how to parse the downloaded page content to extract data.

This is the code for our first Spider. Save it in a file named under the tutorial/spiders directory in your project:

import scrapy

class QuotesSpider(scrapy.Spider):
    name = "quotes"

    def start_requests(self):
        urls = [
        for url in urls:
            yield scrapy.Request(url=url, callback=self.parse)

    def parse(self, response):
        page = response.url.split("/")[-2]
        filename = 'quotes-%s.html' % page
        with open(filename, 'wb') as f:
        self.log('Saved file %s' % filename)

As you can see, our Spider subclasses scrapy.Spider and defines some attributes and methods:

  • name: identifies the Spider. It must be unique within a project, that is, you can’t set the same name for different Spiders.

  • start_requests(): must return an iterable of Requests (you can return a list of requests or write a generator function) which the Spider will begin to crawl from. Subsequent requests will be generated successively from these initial requests.

  • parse(): a method that will be called to handle the response downloaded for each of the requests made. The response parameter is an instance of TextResponse that holds the page content and has further helpful methods to handle it.

    The parse() method usually parses the response, extracting the scraped data as dicts and also finding new URLs to follow and creating new requests (Request) from them.

How to run our spider

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

scrapy crawl quotes

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

... (omitted for brevity)
2016-12-16 21:24:05 [scrapy.core.engine] INFO: Spider opened
2016-12-16 21:24:05 [scrapy.extensions.logstats] INFO: Crawled 0 pages (at 0 pages/min), scraped 0 items (at 0 items/min)
2016-12-16 21:24:05 [scrapy.extensions.telnet] DEBUG: Telnet console listening on
2016-12-16 21:24:05 [scrapy.core.engine] DEBUG: Crawled (404) <GET> (referer: None)
2016-12-16 21:24:05 [scrapy.core.engine] DEBUG: Crawled (200) <GET> (referer: None)
2016-12-16 21:24:05 [scrapy.core.engine] DEBUG: Crawled (200) <GET> (referer: None)
2016-12-16 21:24:05 [quotes] DEBUG: Saved file quotes-1.html
2016-12-16 21:24:05 [quotes] DEBUG: Saved file quotes-2.html
2016-12-16 21:24:05 [scrapy.core.engine] INFO: Closing spider (finished)

Now, check the files in the current directory. You should notice that two new files have been created: quotes-1.html and quotes-2.html, with the content for the respective URLs, as our parse method instructs.


If you are wondering why we haven’t parsed the HTML yet, hold on, we will cover that soon.

What just happened under the hood?

Scrapy schedules the scrapy.Request objects returned by the start_requests method of the Spider. Upon receiving a response for each one, it instantiates Response objects and calls the callback method associated with the request (in this case, the parse method) passing the response as argument.

A shortcut to the start_requests method

Instead of implementing a start_requests() method that generates scrapy.Request objects from URLs, you can just define a start_urls class attribute with a list of URLs. This list will then be used by the default implementation of start_requests() to create the initial requests for your spider:

import scrapy

class QuotesSpider(scrapy.Spider):
    name = "quotes"
    start_urls = [

    def parse(self, response):
        page = response.url.split("/")[-2]
        filename = 'quotes-%s.html' % page
        with open(filename, 'wb') as f:

The parse() method will be called to handle each of the requests for those URLs, even though we haven’t explicitly told Scrapy to do so. This happens because parse() is Scrapy’s default callback method, which is called for requests without an explicitly assigned callback.

Extracting data

The best way to learn how to extract data with Scrapy is trying selectors using the shell Scrapy shell. Run:

scrapy shell ''


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

On Windows, use double quotes instead:

scrapy shell ""

You will see something like:

[ ... Scrapy log here ... ]
2016-09-19 12:09:27 [scrapy.core.engine] DEBUG: Crawled (200) <GET> (referer: None)
[s] Available Scrapy objects:
[s]   scrapy     scrapy module (contains scrapy.Request, scrapy.Selector, etc)
[s]   crawler    <scrapy.crawler.Crawler object at 0x7fa91d888c90>
[s]   item       {}
[s]   request    <GET>
[s]   response   <200>
[s]   settings   <scrapy.settings.Settings object at 0x7fa91d888c10>
[s]   spider     <DefaultSpider 'default' at 0x7fa91c8af990>
[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

Using the shell, you can try selecting elements using CSS with the response object:

>>> response.css('title')
[<Selector xpath='descendant-or-self::title' data='<title>Quotes to Scrape</title>'>]

The result of running response.css('title') is a list-like object called SelectorList, which represents a list of Selector objects that wrap around XML/HTML elements and allow you to run further queries to fine-grain the selection or extract the data.

To extract the text from the title above, you can do:

>>> response.css('title::text').extract()
['Quotes to Scrape']

There are two things to note here: one is that we’ve added ::text to the CSS query, to mean we want to select only the text elements directly inside <title> element. If we don’t specify ::text, we’d get the full title element, including its tags:

>>> response.css('title').extract()
['<title>Quotes to Scrape</title>']

The other thing is that the result of calling .extract() is a list, because we’re dealing with an instance of SelectorList. When you know you just want the first result, as in this case, you can do:

>>> response.css('title::text').extract_first()
'Quotes to Scrape'

As an alternative, you could’ve written:

>>> response.css('title::text')[0].extract()
'Quotes to Scrape'

However, using .extract_first() avoids an IndexError and returns None when it doesn’t find any element matching the selection.

There’s a lesson here: for most scraping code, you want it to be resilient to errors due to things not being found on a page, so that even if some parts fail to be scraped, you can at least get some data.

Besides the extract() and extract_first() methods, you can also use the re() method to extract using regular expressions:

>>> response.css('title::text').re(r'Quotes.*')
['Quotes to Scrape']
>>> response.css('title::text').re(r'Q\w+')
>>> response.css('title::text').re(r'(\w+) to (\w+)')
['Quotes', 'Scrape']

In order to find the proper CSS selectors to use, you might find useful opening the response page from the shell in your web browser using view(response). You can use your browser developer tools or extensions like Firebug (see sections about Using Firebug for scraping and Using Firefox for scraping).

Selector Gadget is also a nice tool to quickly find CSS selector for visually selected elements, which works in many browsers.

XPath: a brief intro

Besides CSS, Scrapy selectors also support using XPath expressions:

>>> response.xpath('//title')
[<Selector xpath='//title' data='<title>Quotes to Scrape</title>'>]
>>> response.xpath('//title/text()').extract_first()
'Quotes to Scrape'

XPath expressions are very powerful, and are the foundation of Scrapy Selectors. In fact, CSS selectors are converted to XPath under-the-hood. You can see that if you read closely the text representation of the selector objects in the shell.

While perhaps not as popular as CSS selectors, XPath expressions offer more power because besides navigating the structure, it can also look at the content. Using XPath, you’re able to select things like: select the link that contains the text “Next Page”. This makes XPath very fitting to the task of scraping, and we encourage you to learn XPath even if you already know how to construct CSS selectors, it will make scraping much easier.

We won’t cover much of XPath here, but you can read more about using XPath with Scrapy Selectors here. To learn more about XPath, we recommend this tutorial to learn XPath through examples, and this tutorial to learn “how to think in XPath”.

Extracting quotes and authors

Now that you know a bit about selection and extraction, let’s complete our spider by writing the code to extract the quotes from the web page.

Each quote in is represented by HTML elements that look like this:

<div class="quote">
    <span class="text">“The world as we have created it is a process of our
    thinking. It cannot be changed without changing our thinking.”</span>
        by <small class="author">Albert Einstein</small>
        <a href="/author/Albert-Einstein">(about)</a>
    <div class="tags">
        <a class="tag" href="/tag/change/page/1/">change</a>
        <a class="tag" href="/tag/deep-thoughts/page/1/">deep-thoughts</a>
        <a class="tag" href="/tag/thinking/page/1/">thinking</a>
        <a class="tag" href="/tag/world/page/1/">world</a>

Let’s open up scrapy shell and play a bit to find out how to extract the data we want:

$ scrapy shell ''

We get a list of selectors for the quote HTML elements with:

>>> response.css("div.quote")

Each of the selectors returned by the query above allows us to run further queries over their sub-elements. Let’s assign the first selector to a variable, so that we can run our CSS selectors directly on a particular quote:

>>> quote = response.css("div.quote")[0]

Now, let’s extract title, author and the tags from that quote using the quote object we just created:

>>> title = quote.css("span.text::text").extract_first()
>>> title
'“The world as we have created it is a process of our thinking. It cannot be changed without changing our thinking.”'
>>> author = quote.css("").extract_first()
>>> author
'Albert Einstein'

Given that the tags are a list of strings, we can use the .extract() method to get all of them:

>>> tags = quote.css("div.tags a.tag::text").extract()
>>> tags
['change', 'deep-thoughts', 'thinking', 'world']

Having figured out how to extract each bit, we can now iterate over all the quotes elements and put them together into a Python dictionary:

>>> for quote in response.css("div.quote"):
...     text = quote.css("span.text::text").extract_first()
...     author = quote.css("").extract_first()
...     tags = quote.css("div.tags a.tag::text").extract()
...     print(dict(text=text, author=author, tags=tags))
{'tags': ['change', 'deep-thoughts', 'thinking', 'world'], 'author': 'Albert Einstein', 'text': '“The world as we have created it is a process of our thinking. It cannot be changed without changing our thinking.”'}
{'tags': ['abilities', 'choices'], 'author': 'J.K. Rowling', 'text': '“It is our choices, Harry, that show what we truly are, far more than our abilities.”'}
    ... a few more of these, omitted for brevity
Extracting data in our spider

Let’s get back to our spider. Until now, it doesn’t extract any data in particular, just saves the whole HTML page to a local file. Let’s integrate the extraction logic above into our spider.

A Scrapy spider typically generates many dictionaries containing the data extracted from the page. To do that, we use the yield Python keyword in the callback, as you can see below:

import scrapy

class QuotesSpider(scrapy.Spider):
    name = "quotes"
    start_urls = [

    def parse(self, response):
        for quote in response.css('div.quote'):
            yield {
                'text': quote.css('span.text::text').extract_first(),
                'author': quote.css('').extract_first(),
                'tags': quote.css('div.tags a.tag::text').extract(),

If you run this spider, it will output the extracted data with the log:

2016-09-19 18:57:19 [scrapy.core.scraper] DEBUG: Scraped from <200>
{'tags': ['life', 'love'], 'author': 'André Gide', 'text': '“It is better to be hated for what you are than to be loved for what you are not.”'}
2016-09-19 18:57:19 [scrapy.core.scraper] DEBUG: Scraped from <200>
{'tags': ['edison', 'failure', 'inspirational', 'paraphrased'], 'author': 'Thomas A. Edison', 'text': "“I have not failed. I've just found 10,000 ways that won't work.”"}

Storing the scraped data

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

scrapy crawl quotes -o quotes.json

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

For historic reasons, Scrapy appends to a given file instead of overwriting its contents. If you run this command twice without removing the file before the second time, you’ll end up with a broken JSON file.

You can also use other formats, like JSON Lines:

scrapy crawl quotes -o quotes.jl

The JSON Lines format is useful because it’s stream-like, you can easily append new records to it. It doesn’t have the same problem of JSON when you run twice. Also, as each record is a separate line, you can process big files without having to fit everything in memory, there are tools like JQ to help doing that at the command-line.

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. A placeholder file for Item Pipelines has been set up for you when the project is created, in tutorial/ Though you don’t need to implement any item pipelines if you just want to store the scraped items.

Using spider arguments

You can provide command line arguments to your spiders by using the -a option when running them:

scrapy crawl quotes -o quotes-humor.json -a tag=humor

These arguments are passed to the Spider’s __init__ method and become spider attributes by default.

In this example, the value provided for the tag argument will be available via self.tag. You can use this to make your spider fetch only quotes with a specific tag, building the URL based on the argument:

import scrapy

class QuotesSpider(scrapy.Spider):
    name = "quotes"

    def start_requests(self):
        url = ''
        tag = getattr(self, 'tag', None)
        if tag is not None:
            url = url + 'tag/' + tag
        yield scrapy.Request(url, self.parse)

    def parse(self, response):
        for quote in response.css('div.quote'):
            yield {
                'text': quote.css('span.text::text').extract_first(),
                'author': quote.css('').extract_first(),

        next_page = response.css(' a::attr(href)').extract_first()
        if next_page is not None:
            yield response.follow(next_page, self.parse)

If you pass the tag=humor argument to this spider, you’ll notice that it will only visit URLs from the humor tag, such as

You can learn more about handling spider arguments here.

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.

You can continue from the section Basic concepts to know more about the command-line tool, spiders, selectors and other things the tutorial hasn’t covered like modeling the scraped data. If you prefer to play with an example project, check the Examples section.


The best way to learn is with examples, and Scrapy is no exception. For this reason, there is an example Scrapy project named quotesbot, that you can use to play and learn more about Scrapy. It contains two spiders for, one using CSS selectors and another one using XPath expressions.

The quotesbot project is available at: You can find more information about it in the project’s README.

If you’re familiar with git, you can checkout the code. Otherwise you can download the project as a zip file by clicking here.

Scrapy at a glance
Understand what Scrapy is and how it can help you.
Installation guide
Get Scrapy installed on your computer.
Scrapy Tutorial
Write your first Scrapy project.
Learn more by playing with a pre-made Scrapy project.

Basic concepts

Command line tool

New in version 0.10.

Scrapy is controlled through the scrapy command-line tool, to be referred here as the “Scrapy tool” to differentiate it from the sub-commands, which we just call “commands” or “Scrapy commands”.

The Scrapy tool provides several commands, for multiple purposes, and each one accepts a different set of arguments and options.

(The scrapy deploy command has been removed in 1.0 in favor of the standalone scrapyd-deploy. See Deploying your project.)

Configuration settings

Scrapy will look for configuration parameters in ini-style scrapy.cfg files in standard locations:

  1. /etc/scrapy.cfg or c:\scrapy\scrapy.cfg (system-wide),
  2. ~/.config/scrapy.cfg ($XDG_CONFIG_HOME) and ~/.scrapy.cfg ($HOME) for global (user-wide) settings, and
  3. scrapy.cfg inside a scrapy project’s root (see next section).

Settings from these files are merged in the listed order of preference: user-defined values have higher priority than system-wide defaults and project-wide settings will override all others, when defined.

Scrapy also understands, and can be configured through, a number of environment variables. Currently these are:

Default structure of Scrapy projects

Before delving into the command-line tool and its sub-commands, let’s first understand the directory structure of a Scrapy project.

Though it can be modified, all Scrapy projects have the same file structure by default, similar to this:


The directory where the scrapy.cfg file resides is known as the project root directory. That file contains the name of the python module that defines the project settings. Here is an example:

default = myproject.settings

Using the scrapy tool

You can start by running the Scrapy tool with no arguments and it will print some usage help and the available commands:

Scrapy X.Y - no active project

  scrapy <command> [options] [args]

Available commands:
  crawl         Run a spider
  fetch         Fetch a URL using the Scrapy downloader

The first line will print the currently active project if you’re inside a Scrapy project. In this example it was run from outside a project. If run from inside a project it would have printed something like this:

Scrapy X.Y - project: myproject

  scrapy <command> [options] [args]

Creating projects

The first thing you typically do with the scrapy tool is create your Scrapy project:

scrapy startproject myproject [project_dir]

That will create a Scrapy project under the project_dir directory. If project_dir wasn’t specified, project_dir will be the same as myproject.

Next, you go inside the new project directory:

cd project_dir

And you’re ready to use the scrapy command to manage and control your project from there.

Controlling projects

You use the scrapy tool from inside your projects to control and manage them.

For example, to create a new spider:

scrapy genspider mydomain

Some Scrapy commands (like crawl) must be run from inside a Scrapy project. See the commands reference below for more information on which commands must be run from inside projects, and which not.

Also keep in mind that some commands may have slightly different behaviours when running them from inside projects. For example, the fetch command will use spider-overridden behaviours (such as the user_agent attribute to override the user-agent) if the url being fetched is associated with some specific spider. This is intentional, as the fetch command is meant to be used to check how spiders are downloading pages.

Available tool commands

This section contains a list of the available built-in commands with a description and some usage examples. Remember, you can always get more info about each command by running:

scrapy <command> -h

And you can see all available commands with:

scrapy -h

There are two kinds of commands, those that only work from inside a Scrapy project (Project-specific commands) and those that also work without an active Scrapy project (Global commands), though they may behave slightly different when running from inside a project (as they would use the project overridden settings).

Global commands:

Project-only commands:

  • Syntax: scrapy startproject <project_name> [project_dir]
  • Requires project: no

Creates a new Scrapy project named project_name, under the project_dir directory. If project_dir wasn’t specified, project_dir will be the same as project_name.

Usage example:

$ scrapy startproject myproject
  • Syntax: scrapy genspider [-t template] <name> <domain>
  • Requires project: no

Create a new spider in the current folder or in the current project’s spiders folder, if called from inside a project. The <name> parameter is set as the spider’s name, while <domain> is used to generate the allowed_domains and start_urls spider’s attributes.

Usage example:

$ scrapy genspider -l
Available templates:

$ scrapy genspider example
Created spider 'example' using template 'basic'

$ scrapy genspider -t crawl scrapyorg
Created spider 'scrapyorg' using template 'crawl'

This is just a convenience shortcut command for creating spiders based on pre-defined templates, but certainly not the only way to create spiders. You can just create the spider source code files yourself, instead of using this command.

  • Syntax: scrapy crawl <spider>
  • Requires project: yes

Start crawling using a spider.

Usage examples:

$ scrapy crawl myspider
[ ... myspider starts crawling ... ]
  • Syntax: scrapy check [-l] <spider>
  • Requires project: yes

Run contract checks.

Usage examples:

$ scrapy check -l
  * parse
  * parse_item
  * parse
  * parse_item

$ scrapy check
[FAILED] first_spider:parse_item
>>> 'RetailPricex' field is missing

[FAILED] first_spider:parse
>>> Returned 92 requests, expected 0..4
  • Syntax: scrapy list
  • Requires project: yes

List all available spiders in the current project. The output is one spider per line.

Usage example:

$ scrapy list
  • Syntax: scrapy edit <spider>
  • Requires project: yes

Edit the given spider using the editor defined in the EDITOR environment variable or (if unset) the EDITOR setting.

This command is provided only as a convenience shortcut for the most common case, the developer is of course free to choose any tool or IDE to write and debug spiders.

Usage example:

$ scrapy edit spider1
  • Syntax: scrapy fetch <url>
  • Requires project: no

Downloads the given URL using the Scrapy downloader and writes the contents to standard output.

The interesting thing about this command is that it fetches the page how the spider would download it. For example, if the spider has a USER_AGENT attribute which overrides the User Agent, it will use that one.

So this command can be used to “see” how your spider would fetch a certain page.

If used outside a project, no particular per-spider behaviour would be applied and it will just use the default Scrapy downloader settings.

Supported options:

  • --spider=SPIDER: bypass spider autodetection and force use of specific spider
  • --headers: print the response’s HTTP headers instead of the response’s body
  • --no-redirect: do not follow HTTP 3xx redirects (default is to follow them)

Usage examples:

$ scrapy fetch --nolog
[ ... html content here ... ]

$ scrapy fetch --nolog --headers
{'Accept-Ranges': ['bytes'],
 'Age': ['1263   '],
 'Connection': ['close     '],
 'Content-Length': ['596'],
 'Content-Type': ['text/html; charset=UTF-8'],
 'Date': ['Wed, 18 Aug 2010 23:59:46 GMT'],
 'Etag': ['"573c1-254-48c9c87349680"'],
 'Last-Modified': ['Fri, 30 Jul 2010 15:30:18 GMT'],
 'Server': ['Apache/2.2.3 (CentOS)']}
  • Syntax: scrapy view <url>
  • Requires project: no

Opens the given URL in a browser, as your Scrapy spider would “see” it. Sometimes spiders see pages differently from regular users, so this can be used to check what the spider “sees” and confirm it’s what you expect.

Supported options:

  • --spider=SPIDER: bypass spider autodetection and force use of specific spider
  • --no-redirect: do not follow HTTP 3xx redirects (default is to follow them)

Usage example:

$ scrapy view
[ ... browser starts ... ]
  • Syntax: scrapy shell [url]
  • Requires project: no

Starts the Scrapy shell for the given URL (if given) or empty if no URL is given. Also supports UNIX-style local file paths, either relative with ./ or ../ prefixes or absolute file paths. See Scrapy shell for more info.

Supported options:

  • --spider=SPIDER: bypass spider autodetection and force use of specific spider
  • -c code: evaluate the code in the shell, print the result and exit
  • --no-redirect: do not follow HTTP 3xx redirects (default is to follow them); this only affects the URL you may pass as argument on the command line; once you are inside the shell, fetch(url) will still follow HTTP redirects by default.

Usage example:

$ scrapy shell
[ ... scrapy shell starts ... ]

$ scrapy shell --nolog -c '(response.status, response.url)'
(200, '')

# shell follows HTTP redirects by default
$ scrapy shell --nolog -c '(response.status, response.url)'
(200, '')

# you can disable this with --no-redirect
# (only for the URL passed as command line argument)
$ scrapy shell --no-redirect --nolog -c '(response.status, response.url)'
(302, '')
  • Syntax: scrapy parse <url> [options]
  • Requires project: yes

Fetches the given URL and parses it with the spider that handles it, using the method passed with the --callback option, or parse if not given.

Supported options:

  • --spider=SPIDER: bypass spider autodetection and force use of specific spider
  • --a NAME=VALUE: set spider argument (may be repeated)
  • --callback or -c: spider method to use as callback for parsing the response
  • --meta or -m: additional request meta that will be passed to the callback request. This must be a valid json string. Example: –meta=’{“foo” : “bar”}’
  • --pipelines: process items through pipelines
  • --rules or -r: use CrawlSpider rules to discover the callback (i.e. spider method) to use for parsing the response
  • --noitems: don’t show scraped items
  • --nolinks: don’t show extracted links
  • --nocolour: avoid using pygments to colorize the output
  • --depth or -d: depth level for which the requests should be followed recursively (default: 1)
  • --verbose or -v: display information for each depth level

Usage example:

$ scrapy parse -c parse_item
[ ... scrapy log lines crawling spider ... ]

# Scraped Items  ------------------------------------------------------------
[{'name': u'Example item',
 'category': u'Furniture',
 'length': u'12 cm'}]

# Requests  -----------------------------------------------------------------
  • Syntax: scrapy settings [options]
  • Requires project: no

Get the value of a Scrapy setting.

If used inside a project it’ll show the project setting value, otherwise it’ll show the default Scrapy value for that setting.

Example usage:

$ scrapy settings --get BOT_NAME
$ scrapy settings --get DOWNLOAD_DELAY
  • Syntax: scrapy runspider <>
  • Requires project: no

Run a spider self-contained in a Python file, without having to create a project.

Example usage:

$ scrapy runspider
[ ... spider starts crawling ... ]
  • Syntax: scrapy version [-v]
  • Requires project: no

Prints the Scrapy version. If used with -v it also prints Python, Twisted and Platform info, which is useful for bug reports.


New in version 0.17.

  • Syntax: scrapy bench
  • Requires project: no

Run a quick benchmark test. Benchmarking.

Custom project commands

You can also add your custom project commands by using the COMMANDS_MODULE setting. See the Scrapy commands in scrapy/commands for examples on how to implement your commands.


Default: '' (empty string)

A module to use for looking up custom Scrapy commands. This is used to add custom commands for your Scrapy project.


COMMANDS_MODULE = 'mybot.commands'
Register commands via entry points


This is an experimental feature, use with caution.

You can also add Scrapy commands from an external library by adding a scrapy.commands section in the entry points of the library file.

The following example adds my_command command:

from setuptools import setup, find_packages

    'scrapy.commands': [


Spiders are classes which define how a certain site (or a group of sites) will be scraped, including how to perform the crawl (i.e. follow links) and how to extract structured data from their pages (i.e. scraping items). In other words, Spiders are the place where you define the custom behaviour for crawling and parsing pages for a particular site (or, in some cases, a group of sites).

For spiders, the scraping cycle goes through something like this:

  1. You start by generating the initial Requests to crawl the first URLs, and specify a callback function to be called with the response downloaded from those requests.

    The first requests to perform are obtained by calling the start_requests() method which (by default) generates Request for the URLs specified in the start_urls and the parse method as callback function for the Requests.

  2. In the callback function, you parse the response (web page) and return either dicts with extracted data, Item objects, Request objects, or an iterable of these objects. Those Requests will also contain a callback (maybe the same) and will then be downloaded by Scrapy and then their response handled by the specified callback.

  3. In callback functions, you parse the page contents, typically using Selectors (but you can also use BeautifulSoup, lxml or whatever mechanism you prefer) and generate items with the parsed data.

  4. Finally, the items returned from the spider will be typically persisted to a database (in some Item Pipeline) or written to a file using Feed exports.

Even though this cycle applies (more or less) to any kind of spider, there are different kinds of default spiders bundled into Scrapy for different purposes. We will talk about those types here.


class scrapy.spiders.Spider

This is the simplest spider, and the one from which every other spider must inherit (including spiders that come bundled with Scrapy, as well as spiders that you write yourself). It doesn’t provide any special functionality. It just provides a default start_requests() implementation which sends requests from the start_urls spider attribute and calls the spider’s method parse for each of the resulting responses.


A string which defines the name for this spider. The spider name is how the spider is located (and instantiated) by Scrapy, so it must be unique. However, nothing prevents you from instantiating more than one instance of the same spider. This is the most important spider attribute and it’s required.

If the spider scrapes a single domain, a common practice is to name the spider after the domain, with or without the TLD. So, for example, a spider that crawls would often be called mywebsite.


In Python 2 this must be ASCII only.


An optional list of strings containing domains that this spider is allowed to crawl. Requests for URLs not belonging to the domain names specified in this list (or their subdomains) won’t be followed if OffsiteMiddleware is enabled.

Let’s say your target url is, then add '' to the list.


A list of URLs where the spider will begin to crawl from, when no particular URLs are specified. So, the first pages downloaded will be those listed here. The subsequent URLs will be generated successively from data contained in the start URLs.


A dictionary of settings that will be overridden from the project wide configuration when running this spider. It must be defined as a class attribute since the settings are updated before instantiation.

For a list of available built-in settings see: Built-in settings reference.


This attribute is set by the from_crawler() class method after initializating the class, and links to the Crawler object to which this spider instance is bound.

Crawlers encapsulate a lot of components in the project for their single entry access (such as extensions, middlewares, signals managers, etc). See Crawler API to know more about them.


Configuration for running this spider. This is a Settings instance, see the Settings topic for a detailed introduction on this subject.


Python logger created with the Spider’s name. You can use it to send log messages through it as described on Logging from Spiders.

from_crawler(crawler, *args, **kwargs)

This is the class method used by Scrapy to create your spiders.

You probably won’t need to override this directly because the default implementation acts as a proxy to the __init__() method, calling it with the given arguments args and named arguments kwargs.

Nonetheless, this method sets the crawler and settings attributes in the new instance so they can be accessed later inside the spider’s code.

  • crawler (Crawler instance) – crawler to which the spider will be bound
  • args (list) – arguments passed to the __init__() method
  • kwargs (dict) – keyword arguments passed to the __init__() method

This method must return an iterable with the first Requests to crawl for this spider. It is called by Scrapy when the spider is opened for scraping. Scrapy calls it only once, so it is safe to implement start_requests() as a generator.

The default implementation generates Request(url, dont_filter=True) for each url in start_urls.

If you want to change the Requests used to start scraping a domain, this is the method to override. For example, if you need to start by logging in using a POST request, you could do:

class MySpider(scrapy.Spider):
    name = 'myspider'

    def start_requests(self):
        return [scrapy.FormRequest("",
                                   formdata={'user': 'john', 'pass': 'secret'},

    def logged_in(self, response):
        # here you would extract links to follow and return Requests for
        # each of them, with another callback

This is the default callback used by Scrapy to process downloaded responses, when their requests don’t specify a callback.

The parse method is in charge of processing the response and returning scraped data and/or more URLs to follow. Other Requests callbacks have the same requirements as the Spider class.

This method, as well as any other Request callback, must return an iterable of Request and/or dicts or Item objects.

Parameters:response (Response) – the response to parse
log(message[, level, component])

Wrapper that sends a log message through the Spider’s logger, kept for backwards compatibility. For more information see Logging from Spiders.


Called when the spider closes. This method provides a shortcut to signals.connect() for the spider_closed signal.

Let’s see an example:

import scrapy

class MySpider(scrapy.Spider):
    name = ''
    allowed_domains = ['']
    start_urls = [

    def parse(self, response):'A response from %s just arrived!', response.url)

Return multiple Requests and items from a single callback:

import scrapy

class MySpider(scrapy.Spider):
    name = ''
    allowed_domains = ['']
    start_urls = [

    def parse(self, response):
        for h3 in response.xpath('//h3').extract():
            yield {"title": h3}

        for url in response.xpath('//a/@href').extract():
            yield scrapy.Request(url, callback=self.parse)

Instead of start_urls you can use start_requests() directly; to give data more structure you can use Items:

import scrapy
from myproject.items import MyItem

class MySpider(scrapy.Spider):
    name = ''
    allowed_domains = ['']

    def start_requests(self):
        yield scrapy.Request('', self.parse)
        yield scrapy.Request('', self.parse)
        yield scrapy.Request('', self.parse)

    def parse(self, response):
        for h3 in response.xpath('//h3').extract():
            yield MyItem(title=h3)

        for url in response.xpath('//a/@href').extract():
            yield scrapy.Request(url, callback=self.parse)

Spider arguments

Spiders can receive arguments that modify their behaviour. Some common uses for spider arguments are to define the start URLs or to restrict the crawl to certain sections of the site, but they can be used to configure any functionality of the spider.

Spider arguments are passed through the crawl command using the -a option. For example:

scrapy crawl myspider -a category=electronics

Spiders can access arguments in their __init__ methods:

import scrapy

class MySpider(scrapy.Spider):
    name = 'myspider'

    def __init__(self, category=None, *args, **kwargs):
        super(MySpider, self).__init__(*args, **kwargs)
        self.start_urls = ['' % category]
        # ...

The default __init__ method will take any spider arguments and copy them to the spider as attributes. The above example can also be written as follows:

import scrapy

class MySpider(scrapy.Spider):
    name = 'myspider'

    def start_requests(self):
        yield scrapy.Request('' % self.category)

Keep in mind that spider arguments are only strings. The spider will not do any parsing on its own. If you were to set the start_urls attribute from the command line, you would have to parse it on your own into a list using something like ast.literal_eval or json.loads and then set it as an attribute. Otherwise, you would cause iteration over a start_urls string (a very common python pitfall) resulting in each character being seen as a separate url.

A valid use case is to set the http auth credentials used by HttpAuthMiddleware or the user agent used by UserAgentMiddleware:

scrapy crawl myspider -a http_user=myuser -a http_pass=mypassword -a user_agent=mybot

Spider arguments can also be passed through the Scrapyd schedule.json API. See Scrapyd documentation.

Generic Spiders

Scrapy comes with some useful generic spiders that you can use to subclass your spiders from. Their aim is to provide convenient functionality for a few common scraping cases, like following all links on a site based on certain rules, crawling from Sitemaps, or parsing an XML/CSV feed.

For the examples used in the following spiders, we’ll assume you have a project with a TestItem declared in a myproject.items module:

import scrapy

class TestItem(scrapy.Item):
    id = scrapy.Field()
    name = scrapy.Field()
    description = scrapy.Field()
class scrapy.spiders.CrawlSpider

This is the most commonly used spider for crawling regular websites, as it provides a convenient mechanism for following links by defining a set of rules. It may not be the best suited for your particular web sites or project, but it’s generic enough for several cases, so you can start from it and override it as needed for more custom functionality, or just implement your own spider.

Apart from the attributes inherited from Spider (that you must specify), this class supports a new attribute:


Which is a list of one (or more) Rule objects. Each Rule defines a certain behaviour for crawling the site. Rules objects are described below. If multiple rules match the same link, the first one will be used, according to the order they’re defined in this attribute.

This spider also exposes an overrideable method:


This method is called for the start_urls responses. It allows to parse the initial responses and must return either an Item object, a Request object, or an iterable containing any of them.

Crawling rules
class scrapy.spiders.Rule(link_extractor, callback=None, cb_kwargs=None, follow=None, process_links=None, process_request=None)

link_extractor is a Link Extractor object which defines how links will be extracted from each crawled page.

callback is a callable or a string (in which case a method from the spider object with that name will be used) to be called for each link extracted with the specified link_extractor. This callback receives a response as its first argument and must return a list containing Item and/or Request objects (or any subclass of them).


When writing crawl spider rules, avoid using parse as callback, since the CrawlSpider uses the parse method itself to implement its logic. So if you override the parse method, the crawl spider will no longer work.

cb_kwargs is a dict containing the keyword arguments to be passed to the callback function.

follow is a boolean which specifies if links should be followed from each response extracted with this rule. If callback is None follow defaults to True, otherwise it defaults to False.

process_links is a callable, or a string (in which case a method from the spider object with that name will be used) which will be called for each list of links extracted from each response using the specified link_extractor. This is mainly used for filtering purposes.

process_request is a callable, or a string (in which case a method from the spider object with that name will be used) which will be called with every request extracted by this rule, and must return a request or None (to filter out the request).

CrawlSpider example

Let’s now take a look at an example CrawlSpider with rules:

import scrapy
from scrapy.spiders import CrawlSpider, Rule
from scrapy.linkextractors import LinkExtractor

class MySpider(CrawlSpider):
    name = ''
    allowed_domains = ['']
    start_urls = ['']

    rules = (
        # Extract links matching 'category.php' (but not matching 'subsection.php')
        # and follow links from them (since no callback means follow=True by default).
        Rule(LinkExtractor(allow=('category\.php', ), deny=('subsection\.php', ))),

        # Extract links matching 'item.php' and parse them with the spider's method parse_item
        Rule(LinkExtractor(allow=('item\.php', )), callback='parse_item'),

    def parse_item(self, response):'Hi, this is an item page! %s', response.url)
        item = scrapy.Item()
        item['id'] = response.xpath('//td[@id="item_id"]/text()').re(r'ID: (\d+)')
        item['name'] = response.xpath('//td[@id="item_name"]/text()').extract()
        item['description'] = response.xpath('//td[@id="item_description"]/text()').extract()
        return item

This spider would start crawling’s home page, collecting category links, and item links, parsing the latter with the parse_item method. For each item response, some data will be extracted from the HTML using XPath, and an Item will be filled with it.

class scrapy.spiders.XMLFeedSpider

XMLFeedSpider is designed for parsing XML feeds by iterating through them by a certain node name. The iterator can be chosen from: iternodes, xml, and html. It’s recommended to use the iternodes iterator for performance reasons, since the xml and html iterators generate the whole DOM at once in order to parse it. However, using html as the iterator may be useful when parsing XML with bad markup.

To set the iterator and the tag name, you must define the following class attributes:


A string which defines the iterator to use. It can be either:

  • 'iternodes' - a fast iterator based on regular expressions
  • 'html' - an iterator which uses Selector. Keep in mind this uses DOM parsing and must load all DOM in memory which could be a problem for big feeds
  • 'xml' - an iterator which uses Selector. Keep in mind this uses DOM parsing and must load all DOM in memory which could be a problem for big feeds

It defaults to: 'iternodes'.


A string with the name of the node (or element) to iterate in. Example:

itertag = 'product'

A list of (prefix, uri) tuples which define the namespaces available in that document that will be processed with this spider. The prefix and uri will be used to automatically register namespaces using the register_namespace() method.

You can then specify nodes with namespaces in the itertag attribute.


class YourSpider(XMLFeedSpider):

    namespaces = [('n', '')]
    itertag = 'n:url'
    # ...

Apart from these new attributes, this spider has the following overrideable methods too:


A method that receives the response as soon as it arrives from the spider middleware, before the spider starts parsing it. It can be used to modify the response body before parsing it. This method receives a response and also returns a response (it could be the same or another one).

parse_node(response, selector)

This method is called for the nodes matching the provided tag name (itertag). Receives the response and an Selector for each node. Overriding this method is mandatory. Otherwise, you spider won’t work. This method must return either a Item object, a Request object, or an iterable containing any of them.

process_results(response, results)

This method is called for each result (item or request) returned by the spider, and it’s intended to perform any last time processing required before returning the results to the framework core, for example setting the item IDs. It receives a list of results and the response which originated those results. It must return a list of results (Items or Requests).

XMLFeedSpider example

These spiders are pretty easy to use, let’s have a look at one example:

from scrapy.spiders import XMLFeedSpider
from myproject.items import TestItem

class MySpider(XMLFeedSpider):
    name = ''
    allowed_domains = ['']
    start_urls = ['']
    iterator = 'iternodes'  # This is actually unnecessary, since it's the default value
    itertag = 'item'

    def parse_node(self, response, node):'Hi, this is a <%s> node!: %s', self.itertag, ''.join(node.extract()))

        item = TestItem()
        item['id'] = node.xpath('@id').extract()
        item['name'] = node.xpath('name').extract()
        item['description'] = node.xpath('description').extract()
        return item

Basically what we did up there was to create a spider that downloads a feed from the given start_urls, and then iterates through each of its item tags, prints them out, and stores some random data in an Item.

class scrapy.spiders.CSVFeedSpider

This spider is very similar to the XMLFeedSpider, except that it iterates over rows, instead of nodes. The method that gets called in each iteration is parse_row().


A string with the separator character for each field in the CSV file Defaults to ',' (comma).


A string with the enclosure character for each field in the CSV file Defaults to '"' (quotation mark).


A list of the column names in the CSV file.

parse_row(response, row)

Receives a response and a dict (representing each row) with a key for each provided (or detected) header of the CSV file. This spider also gives the opportunity to override adapt_response and process_results methods for pre- and post-processing purposes.

CSVFeedSpider example

Let’s see an example similar to the previous one, but using a CSVFeedSpider:

from scrapy.spiders import CSVFeedSpider
from myproject.items import TestItem

class MySpider(CSVFeedSpider):
    name = ''
    allowed_domains = ['']
    start_urls = ['']
    delimiter = ';'
    quotechar = "'"
    headers = ['id', 'name', 'description']

    def parse_row(self, response, row):'Hi, this is a row!: %r', row)

        item = TestItem()
        item['id'] = row['id']
        item['name'] = row['name']
        item['description'] = row['description']
        return item
class scrapy.spiders.SitemapSpider

SitemapSpider allows you to crawl a site by discovering the URLs using Sitemaps.

It supports nested sitemaps and discovering sitemap urls from robots.txt.


A list of urls pointing to the sitemaps whose urls you want to crawl.

You can also point to a robots.txt and it will be parsed to extract sitemap urls from it.


A list of tuples (regex, callback) where:

  • regex is a regular expression to match urls extracted from sitemaps. regex can be either a str or a compiled regex object.
  • callback is the callback to use for processing the urls that match the regular expression. callback can be a string (indicating the name of a spider method) or a callable.

For example:

sitemap_rules = [('/product/', 'parse_product')]

Rules are applied in order, and only the first one that matches will be used.

If you omit this attribute, all urls found in sitemaps will be processed with the parse callback.


A list of regexes of sitemap that should be followed. This is is only for sites that use Sitemap index files that point to other sitemap files.

By default, all sitemaps are followed.

Specifies if alternate links for one url should be followed. These are links for the same website in another language passed within the same url block.

For example:

    <xhtml:link rel="alternate" hreflang="de" href=""/>

With sitemap_alternate_links set, this would retrieve both URLs. With sitemap_alternate_links disabled, only would be retrieved.

Default is sitemap_alternate_links disabled.

SitemapSpider examples

Simplest example: process all urls discovered through sitemaps using the parse callback:

from scrapy.spiders import SitemapSpider

class MySpider(SitemapSpider):
    sitemap_urls = ['']

    def parse(self, response):
        pass # ... scrape item here ...

Process some urls with certain callback and other urls with a different callback:

from scrapy.spiders import SitemapSpider

class MySpider(SitemapSpider):
    sitemap_urls = ['']
    sitemap_rules = [
        ('/product/', 'parse_product'),
        ('/category/', 'parse_category'),

    def parse_product(self, response):
        pass # ... scrape product ...

    def parse_category(self, response):
        pass # ... scrape category ...

Follow sitemaps defined in the robots.txt file and only follow sitemaps whose url contains /sitemap_shop:

from scrapy.spiders import SitemapSpider

class MySpider(SitemapSpider):
    sitemap_urls = ['']
    sitemap_rules = [
        ('/shop/', 'parse_shop'),
    sitemap_follow = ['/sitemap_shops']

    def parse_shop(self, response):
        pass # ... scrape shop here ...

Combine SitemapSpider with other sources of urls:

from scrapy.spiders import SitemapSpider

class MySpider(SitemapSpider):
    sitemap_urls = ['']
    sitemap_rules = [
        ('/shop/', 'parse_shop'),

    other_urls = ['']

    def start_requests(self):
        requests = list(super(MySpider, self).start_requests())
        requests += [scrapy.Request(x, self.parse_other) for x in self.other_urls]
        return requests

    def parse_shop(self, response):
        pass # ... scrape shop here ...

    def parse_other(self, response):
        pass # ... scrape other here ...


When you’re scraping web pages, the most common task you need to perform is to extract data from the HTML source. There are several libraries available to achieve this:

  • BeautifulSoup is a very popular web scraping library among Python programmers which constructs a Python object based on the structure of the HTML code and also deals with bad markup reasonably well, but it has one drawback: it’s slow.
  • lxml is an XML parsing library (which also parses HTML) with a pythonic API based on ElementTree. (lxml is not part of the Python standard library.)

Scrapy comes with its own mechanism for extracting data. They’re called selectors because they “select” certain parts of the HTML document specified either by XPath or CSS expressions.

XPath is a language for selecting nodes in XML documents, which can also be used with HTML. CSS is a language for applying styles to HTML documents. It defines selectors to associate those styles with specific HTML elements.

Scrapy selectors are built over the lxml library, which means they’re very similar in speed and parsing accuracy.

This page explains how selectors work and describes their API which is very small and simple, unlike the lxml API which is much bigger because the lxml library can be used for many other tasks, besides selecting markup documents.

For a complete reference of the selectors API see Selector reference

Using selectors

Constructing selectors

Scrapy selectors are instances of Selector class constructed by passing text or TextResponse object. It automatically chooses the best parsing rules (XML vs HTML) based on input type:

>>> from scrapy.selector import Selector
>>> from scrapy.http import HtmlResponse

Constructing from text:

>>> body = '<html><body><span>good</span></body></html>'
>>> Selector(text=body).xpath('//span/text()').extract()

Constructing from response:

>>> response = HtmlResponse(url='', body=body)
>>> Selector(response=response).xpath('//span/text()').extract()

For convenience, response objects expose a selector on .selector attribute, it’s totally OK to use this shortcut when possible:

>>> response.selector.xpath('//span/text()').extract()
Using selectors

To explain how to use the selectors we’ll use the Scrapy shell (which provides interactive testing) and an example page located in the Scrapy documentation server:

Here’s its HTML code:

  <base href='' />
  <title>Example website</title>
  <div id='images'>
   <a href='image1.html'>Name: My image 1 <br /><img src='image1_thumb.jpg' /></a>
   <a href='image2.html'>Name: My image 2 <br /><img src='image2_thumb.jpg' /></a>
   <a href='image3.html'>Name: My image 3 <br /><img src='image3_thumb.jpg' /></a>
   <a href='image4.html'>Name: My image 4 <br /><img src='image4_thumb.jpg' /></a>
   <a href='image5.html'>Name: My image 5 <br /><img src='image5_thumb.jpg' /></a>

First, let’s open the shell:

scrapy shell

Then, after the shell loads, you’ll have the response available as response shell variable, and its attached selector in response.selector attribute.

Since we’re dealing with HTML, the selector will automatically use an HTML parser.

So, by looking at the HTML code of that page, let’s construct an XPath for selecting the text inside the title tag:

>>> response.selector.xpath('//title/text()')
[<Selector (text) xpath=//title/text()>]

Querying responses using XPath and CSS is so common that responses include two convenience shortcuts: response.xpath() and response.css():

>>> response.xpath('//title/text()')
[<Selector (text) xpath=//title/text()>]
>>> response.css('title::text')
[<Selector (text) xpath=//title/text()>]

As you can see, .xpath() and .css() methods return a SelectorList instance, which is a list of new selectors. This API can be used for quickly selecting nested data:

>>> response.css('img').xpath('@src').extract()

To actually extract the textual data, you must call the selector .extract() method, as follows:

>>> response.xpath('//title/text()').extract()
[u'Example website']

If you want to extract only first matched element, you can call the selector .extract_first()

>>> response.xpath('//div[@id="images"]/a/text()').extract_first()
u'Name: My image 1 '

It returns None if no element was found:

>>> response.xpath('//div[@id="not-exists"]/text()').extract_first() is None

A default return value can be provided as an argument, to be used instead of None:

>>> response.xpath('//div[@id="not-exists"]/text()').extract_first(default='not-found')

Notice that CSS selectors can select text or attribute nodes using CSS3 pseudo-elements:

>>> response.css('title::text').extract()
[u'Example website']

Now we’re going to get the base URL and some image links:

>>> response.xpath('//base/@href').extract()

>>> response.css('base::attr(href)').extract()

>>> response.xpath('//a[contains(@href, "image")]/@href').extract()

>>> response.css('a[href*=image]::attr(href)').extract()

>>> response.xpath('//a[contains(@href, "image")]/img/@src').extract()

>>> response.css('a[href*=image] img::attr(src)').extract()
Nesting selectors

The selection methods (.xpath() or .css()) return a list of selectors of the same type, so you can call the selection methods for those selectors too. Here’s an example:

>>> links = response.xpath('//a[contains(@href, "image")]')
>>> links.extract()
[u'<a href="image1.html">Name: My image 1 <br><img src="image1_thumb.jpg"></a>',
 u'<a href="image2.html">Name: My image 2 <br><img src="image2_thumb.jpg"></a>',
 u'<a href="image3.html">Name: My image 3 <br><img src="image3_thumb.jpg"></a>',
 u'<a href="image4.html">Name: My image 4 <br><img src="image4_thumb.jpg"></a>',
 u'<a href="image5.html">Name: My image 5 <br><img src="image5_thumb.jpg"></a>']

>>> for index, link in enumerate(links):
...     args = (index, link.xpath('@href').extract(), link.xpath('img/@src').extract())
...     print 'Link number %d points to url %s and image %s' % args

Link number 0 points to url [u'image1.html'] and image [u'image1_thumb.jpg']
Link number 1 points to url [u'image2.html'] and image [u'image2_thumb.jpg']
Link number 2 points to url [u'image3.html'] and image [u'image3_thumb.jpg']
Link number 3 points to url [u'image4.html'] and image [u'image4_thumb.jpg']
Link number 4 points to url [u'image5.html'] and image [u'image5_thumb.jpg']
Using selectors with regular expressions

Selector also has a .re() method for extracting data using regular expressions. However, unlike using .xpath() or .css() methods, .re() returns a list of unicode strings. So you can’t construct nested .re() calls.

Here’s an example used to extract image names from the HTML code above:

>>> response.xpath('//a[contains(@href, "image")]/text()').re(r'Name:\s*(.*)')
[u'My image 1',
 u'My image 2',
 u'My image 3',
 u'My image 4',
 u'My image 5']

There’s an additional helper reciprocating .extract_first() for .re(), named .re_first(). Use it to extract just the first matching string:

>>> response.xpath('//a[contains(@href, "image")]/text()').re_first(r'Name:\s*(.*)')
u'My image 1'
Working with relative XPaths

Keep in mind that if you are nesting selectors and use an XPath that starts with /, that XPath will be absolute to the document and not relative to the Selector you’re calling it from.

For example, suppose you want to extract all <p> elements inside <div> elements. First, you would get all <div> elements:

>>> divs = response.xpath('//div')

At first, you may be tempted to use the following approach, which is wrong, as it actually extracts all <p> elements from the document, not only those inside <div> elements:

>>> for p in divs.xpath('//p'):  # this is wrong - gets all <p> from the whole document
...     print p.extract()

This is the proper way to do it (note the dot prefixing the .//p XPath):

>>> for p in divs.xpath('.//p'):  # extracts all <p> inside
...     print p.extract()

Another common case would be to extract all direct <p> children:

>>> for p in divs.xpath('p'):
...     print p.extract()

For more details about relative XPaths see the Location Paths section in the XPath specification.

Variables in XPath expressions

XPath allows you to reference variables in your XPath expressions, using the $somevariable syntax. This is somewhat similar to parameterized queries or prepared statements in the SQL world where you replace some arguments in your queries with placeholders like ?, which are then substituted with values passed with the query.

Here’s an example to match an element based on its “id” attribute value, without hard-coding it (that was shown previously):

>>> # `$val` used in the expression, a `val` argument needs to be passed
>>> response.xpath('//div[@id=$val]/a/text()', val='images').extract_first()
u'Name: My image 1 '

Here’s another example, to find the “id” attribute of a <div> tag containing five <a> children (here we pass the value 5 as an integer):

>>> response.xpath('//div[count(a)=$cnt]/@id', cnt=5).extract_first()

All variable references must have a binding value when calling .xpath() (otherwise you’ll get a ValueError: XPath error: exception). This is done by passing as many named arguments as necessary.

parsel, the library powering Scrapy selectors, has more details and examples on XPath variables.

Using EXSLT extensions

Being built atop lxml, Scrapy selectors also support some EXSLT extensions and come with these pre-registered namespaces to use in XPath expressions:

prefix namespace usage
re regular expressions
set set manipulation
Regular expressions

The test() function, for example, can prove quite useful when XPath’s starts-with() or contains() are not sufficient.

Example selecting links in list item with a “class” attribute ending with a digit:

>>> from scrapy import Selector
>>> doc = """
... <div>
...     <ul>
...         <li class="item-0"><a href="link1.html">first item</a></li>
...         <li class="item-1"><a href="link2.html">second item</a></li>
...         <li class="item-inactive"><a href="link3.html">third item</a></li>
...         <li class="item-1"><a href="link4.html">fourth item</a></li>
...         <li class="item-0"><a href="link5.html">fifth item</a></li>
...     </ul>
... </div>
... """
>>> sel = Selector(text=doc, type="html")
>>> sel.xpath('//li//@href').extract()
[u'link1.html', u'link2.html', u'link3.html', u'link4.html', u'link5.html']
>>> sel.xpath('//li[re:test(@class, "item-\d$")]//@href').extract()
[u'link1.html', u'link2.html', u'link4.html', u'link5.html']


C library libxslt doesn’t natively support EXSLT regular expressions so lxml’s implementation uses hooks to Python’s re module. Thus, using regexp functions in your XPath expressions may add a small performance penalty.

Set operations

These can be handy for excluding parts of a document tree before extracting text elements for example.

Example extracting microdata (sample content taken from with groups of itemscopes and corresponding itemprops:

>>> doc = """
... <div itemscope itemtype="">
...   <span itemprop="name">Kenmore White 17" Microwave</span>
...   <img src="kenmore-microwave-17in.jpg" alt='Kenmore 17" Microwave' />
...   <div itemprop="aggregateRating"
...     itemscope itemtype="">
...    Rated <span itemprop="ratingValue">3.5</span>/5
...    based on <span itemprop="reviewCount">11</span> customer reviews
...   </div>
...   <div itemprop="offers" itemscope itemtype="">
...     <span itemprop="price">$55.00</span>
...     <link itemprop="availability" href="" />In stock
...   </div>
...   Product description:
...   <span itemprop="description">0.7 cubic feet countertop microwave.
...   Has six preset cooking categories and convenience features like
...   Add-A-Minute and Child Lock.</span>
...   Customer reviews:
...   <div itemprop="review" itemscope itemtype="">
...     <span itemprop="name">Not a happy camper</span> -
...     by <span itemprop="author">Ellie</span>,
...     <meta itemprop="datePublished" content="2011-04-01">April 1, 2011
...     <div itemprop="reviewRating" itemscope itemtype="">
...       <meta itemprop="worstRating" content = "1">
...       <span itemprop="ratingValue">1</span>/
...       <span itemprop="bestRating">5</span>stars
...     </div>
...     <span itemprop="description">The lamp burned out and now I have to replace
...     it. </span>
...   </div>
...   <div itemprop="review" itemscope itemtype="">
...     <span itemprop="name">Value purchase</span> -
...     by <span itemprop="author">Lucas</span>,
...     <meta itemprop="datePublished" content="2011-03-25">March 25, 2011
...     <div itemprop="reviewRating" itemscope itemtype="">
...       <meta itemprop="worstRating" content = "1"/>
...       <span itemprop="ratingValue">4</span>/
...       <span itemprop="bestRating">5</span>stars
...     </div>
...     <span itemprop="description">Great microwave for the price. It is small and
...     fits in my apartment.</span>
...   </div>
...   ...
... </div>
... """
>>> sel = Selector(text=doc, type="html")
>>> for scope in sel.xpath('//div[@itemscope]'):
...     print "current scope:", scope.xpath('@itemtype').extract()
...     props = scope.xpath('''
...                 set:difference(./descendant::*/@itemprop,
...                                .//*[@itemscope]/*/@itemprop)''')
...     print "    properties:", props.extract()
...     print

current scope: [u'']
    properties: [u'name', u'aggregateRating', u'offers', u'description', u'review', u'review']

current scope: [u'']
    properties: [u'ratingValue', u'reviewCount']

current scope: [u'']
    properties: [u'price', u'availability']

current scope: [u'']
    properties: [u'name', u'author', u'datePublished', u'reviewRating', u'description']

current scope: [u'']
    properties: [u'worstRating', u'ratingValue', u'bestRating']

current scope: [u'']
    properties: [u'name', u'author', u'datePublished', u'reviewRating', u'description']

current scope: [u'']
    properties: [u'worstRating', u'ratingValue', u'bestRating']


Here we first iterate over itemscope elements, and for each one, we look for all itemprops elements and exclude those that are themselves inside another itemscope.

Some XPath tips

Here are some tips that you may find useful when using XPath with Scrapy selectors, based on this post from ScrapingHub’s blog. If you are not much familiar with XPath yet, you may want to take a look first at this XPath tutorial.

Using text nodes in a condition

When you need to use the text content as argument to an XPath string function, avoid using .//text() and use just . instead.

This is because the expression .//text() yields a collection of text elements – a node-set. And when a node-set is converted to a string, which happens when it is passed as argument to a string function like contains() or starts-with(), it results in the text for the first element only.


>>> from scrapy import Selector
>>> sel = Selector(text='<a href="#">Click here to go to the <strong>Next Page</strong></a>')

Converting a node-set to string:

>>> sel.xpath('//a//text()').extract() # take a peek at the node-set
[u'Click here to go to the ', u'Next Page']
>>> sel.xpath("string(//a[1]//text())").extract() # convert it to string
[u'Click here to go to the ']

A node converted to a string, however, puts together the text of itself plus of all its descendants:

>>> sel.xpath("//a[1]").extract() # select the first node
[u'<a href="#">Click here to go to the <strong>Next Page</strong></a>']
>>> sel.xpath("string(//a[1])").extract() # convert it to string
[u'Click here to go to the Next Page']

So, using the .//text() node-set won’t select anything in this case:

>>> sel.xpath("//a[contains(.//text(), 'Next Page')]").extract()

But using the . to mean the node, works:

>>> sel.xpath("//a[contains(., 'Next Page')]").extract()
[u'<a href="#">Click here to go to the <strong>Next Page</strong></a>']
Beware of the difference between //node[1] and (//node)[1]

//node[1] selects all the nodes occurring first under their respective parents.

(//node)[1] selects all the nodes in the document, and then gets only the first of them.


>>> from scrapy import Selector
>>> sel = Selector(text="""
....:     <ul class="list">
....:         <li>1</li>
....:         <li>2</li>
....:         <li>3</li>
....:     </ul>
....:     <ul class="list">
....:         <li>4</li>
....:         <li>5</li>
....:         <li>6</li>
....:     </ul>""")
>>> xp = lambda x: sel.xpath(x).extract()

This gets all first <li> elements under whatever it is its parent:

>>> xp("//li[1]")
[u'<li>1</li>', u'<li>4</li>']

And this gets the first <li> element in the whole document:

>>> xp("(//li)[1]")

This gets all first <li> elements under an <ul> parent:

>>> xp("//ul/li[1]")
[u'<li>1</li>', u'<li>4</li>']

And this gets the first <li> element under an <ul> parent in the whole document:

>>> xp("(//ul/li)[1]")
When querying by class, consider using CSS

Because an element can contain multiple CSS classes, the XPath way to select elements by class is the rather verbose:

*[contains(concat(' ', normalize-space(@class), ' '), ' someclass ')]

If you use @class='someclass' you may end up missing elements that have other classes, and if you just use contains(@class, 'someclass') to make up for that you may end up with more elements that you want, if they have a different class name that shares the string someclass.

As it turns out, Scrapy selectors allow you to chain selectors, so most of the time you can just select by class using CSS and then switch to XPath when needed:

>>> from scrapy import Selector
>>> sel = Selector(text='<div class="hero shout"><time datetime="2014-07-23 19:00">Special date</time></div>')
>>> sel.css('.shout').xpath('./time/@datetime').extract()
[u'2014-07-23 19:00']

This is cleaner than using the verbose XPath trick shown above. Just remember to use the . in the XPath expressions that will follow.

Built-in Selectors reference

Selector objects
class scrapy.selector.Selector(response=None, text=None, type=None)

An instance of Selector is a wrapper over response to select certain parts of its content.

response is an HtmlResponse or an XmlResponse object that will be used for selecting and extracting data.

text is a unicode string or utf-8 encoded text for cases when a response isn’t available. Using text and response together is undefined behavior.

type defines the selector type, it can be "html", "xml" or None (default).

If type is None, the selector automatically chooses the best type based on response type (see below), or defaults to "html" in case it is used together with text.

If type is None and a response is passed, the selector type is inferred from the response type as follows:

Otherwise, if type is set, the selector type will be forced and no detection will occur.


Find nodes matching the xpath query and return the result as a SelectorList instance with all elements flattened. List elements implement Selector interface too.

query is a string containing the XPATH query to apply.


For convenience, this method can be called as response.xpath()


Apply the given CSS selector and return a SelectorList instance.

query is a string containing the CSS selector to apply.

In the background, CSS queries are translated into XPath queries using cssselect library and run .xpath() method.


For convenience this method can be called as response.css()


Serialize and return the matched nodes as a list of unicode strings. Percent encoded content is unquoted.


Apply the given regex and return a list of unicode strings with the matches.

regex can be either a compiled regular expression or a string which will be compiled to a regular expression using re.compile(regex)


Note that re() and re_first() both decode HTML entities (except &lt; and &amp;).

register_namespace(prefix, uri)

Register the given namespace to be used in this Selector. Without registering namespaces you can’t select or extract data from non-standard namespaces. See examples below.


Remove all namespaces, allowing to traverse the document using namespace-less xpaths. See example below.


Returns True if there is any real content selected or False otherwise. In other words, the boolean value of a Selector is given by the contents it selects.

SelectorList objects
class scrapy.selector.SelectorList

The SelectorList class is a subclass of the builtin list class, which provides a few additional methods.


Call the .xpath() method for each element in this list and return their results flattened as another SelectorList.

query is the same argument as the one in Selector.xpath()


Call the .css() method for each element in this list and return their results flattened as another SelectorList.

query is the same argument as the one in Selector.css()


Call the .extract() method for each element in this list and return their results flattened, as a list of unicode strings.


Call the .re() method for each element in this list and return their results flattened, as a list of unicode strings.

Selector examples on HTML response

Here’s a couple of Selector examples to illustrate several concepts. In all cases, we assume there is already a Selector instantiated with a HtmlResponse object like this:

sel = Selector(html_response)
  1. Select all <h1> elements from an HTML response body, returning a list of Selector objects (ie. a SelectorList object):

  2. Extract the text of all <h1> elements from an HTML response body, returning a list of unicode strings:

    sel.xpath("//h1").extract()         # this includes the h1 tag
    sel.xpath("//h1/text()").extract()  # this excludes the h1 tag
  3. Iterate over all <p> tags and print their class attribute:

    for node in sel.xpath("//p"):
        print node.xpath("@class").extract()
Selector examples on XML response

Here’s a couple of examples to illustrate several concepts. In both cases we assume there is already a Selector instantiated with an XmlResponse object like this:

sel = Selector(xml_response)
  1. Select all <product> elements from an XML response body, returning a list of Selector objects (ie. a SelectorList object):

  2. Extract all prices from a Google Base XML feed which requires registering a namespace:

    sel.register_namespace("g", "")
Removing namespaces

When dealing with scraping projects, it is often quite convenient to get rid of namespaces altogether and just work with element names, to write more simple/convenient XPaths. You can use the Selector.remove_namespaces() method for that.

Let’s show an example that illustrates this with GitHub blog atom feed.

First, we open the shell with the url we want to scrape:

$ scrapy shell

Once in the shell we can try selecting all <link> objects and see that it doesn’t work (because the Atom XML namespace is obfuscating those nodes):

>>> response.xpath("//link")

But once we call the Selector.remove_namespaces() method, all nodes can be accessed directly by their names:

>>> response.selector.remove_namespaces()
>>> response.xpath("//link")
[<Selector xpath='//link' data=u'<link xmlns="'>,
 <Selector xpath='//link' data=u'<link xmlns="'>,

If you wonder why the namespace removal procedure isn’t always called by default instead of having to call it manually, this is because of two reasons, which, in order of relevance, are:

  1. Removing namespaces requires to iterate and modify all nodes in the document, which is a reasonably expensive operation to perform for all documents crawled by Scrapy
  2. There could be some cases where using namespaces is actually required, in case some element names clash between namespaces. These cases are very rare though.


The main goal in scraping is to extract structured data from unstructured sources, typically, web pages. Scrapy spiders can return the extracted data as Python dicts. While convenient and familiar, Python dicts lack structure: it is easy to make a typo in a field name or return inconsistent data, especially in a larger project with many spiders.

To define common output data format Scrapy provides the Item class. Item objects are simple containers used to collect the scraped data. They provide a dictionary-like API with a convenient syntax for declaring their available fields.

Various Scrapy components use extra information provided by Items: exporters look at declared fields to figure out columns to export, serialization can be customized using Item fields metadata, trackref tracks Item instances to help find memory leaks (see Debugging memory leaks with trackref), etc.

Declaring Items

Items are declared using a simple class definition syntax and Field objects. Here is an example:

import scrapy

class Product(scrapy.Item):
    name = scrapy.Field()
    price = scrapy.Field()
    stock = scrapy.Field()
    last_updated = scrapy.Field(serializer=str)


Those familiar with Django will notice that Scrapy Items are declared similar to Django Models, except that Scrapy Items are much simpler as there is no concept of different field types.

Item Fields

Field objects are used to specify metadata for each field. For example, the serializer function for the last_updated field illustrated in the example above.

You can specify any kind of metadata for each field. There is no restriction on the values accepted by Field objects. For this same reason, there is no reference list of all available metadata keys. Each key defined in Field objects could be used by a different component, and only those components know about it. You can also define and use any other Field key in your project too, for your own needs. The main goal of Field objects is to provide a way to define all field metadata in one place. Typically, those components whose behaviour depends on each field use certain field keys to configure that behaviour. You must refer to their documentation to see which metadata keys are used by each component.

It’s important to note that the Field objects used to declare the item do not stay assigned as class attributes. Instead, they can be accessed through the Item.fields attribute.

Working with Items

Here are some examples of common tasks performed with items, using the Product item declared above. You will notice the API is very similar to the dict API.

Creating items
>>> product = Product(name='Desktop PC', price=1000)
>>> print product
Product(name='Desktop PC', price=1000)
Getting field values
>>> product['name']
Desktop PC
>>> product.get('name')
Desktop PC

>>> product['price']

>>> product['last_updated']
Traceback (most recent call last):
KeyError: 'last_updated'

>>> product.get('last_updated', 'not set')
not set

>>> product['lala'] # getting unknown field
Traceback (most recent call last):
KeyError: 'lala'

>>> product.get('lala', 'unknown field')
'unknown field'

>>> 'name' in product  # is name field populated?

>>> 'last_updated' in product  # is last_updated populated?

>>> 'last_updated' in product.fields  # is last_updated a declared field?

>>> 'lala' in product.fields  # is lala a declared field?
Setting field values
>>> product['last_updated'] = 'today'
>>> product['last_updated']

>>> product['lala'] = 'test' # setting unknown field
Traceback (most recent call last):
KeyError: 'Product does not support field: lala'
Accessing all populated values

To access all populated values, just use the typical dict API:

>>> product.keys()
['price', 'name']

>>> product.items()
[('price', 1000), ('name', 'Desktop PC')]
Other common tasks

Copying items:

>>> product2 = Product(product)
>>> print product2
Product(name='Desktop PC', price=1000)

>>> product3 = product2.copy()
>>> print product3
Product(name='Desktop PC', price=1000)

Creating dicts from items:

>>> dict(product) # create a dict from all populated values
{'price': 1000, 'name': 'Desktop PC'}

Creating items from dicts:

>>> Product({'name': 'Laptop PC', 'price': 1500})
Product(price=1500, name='Laptop PC')

>>> Product({'name': 'Laptop PC', 'lala': 1500}) # warning: unknown field in dict
Traceback (most recent call last):
KeyError: 'Product does not support field: lala'

Extending Items

You can extend Items (to add more fields or to change some metadata for some fields) by declaring a subclass of your original Item.

For example:

class DiscountedProduct(Product):
    discount_percent = scrapy.Field(serializer=str)
    discount_expiration_date = scrapy.Field()

You can also extend field metadata by using the previous field metadata and appending more values, or changing existing values, like this:

class SpecificProduct(Product):
    name = scrapy.Field(Product.fields['name'], serializer=my_serializer)

That adds (or replaces) the serializer metadata key for the name field, keeping all the previously existing metadata values.

Item objects

class scrapy.item.Item([arg])

Return a new Item optionally initialized from the given argument.

Items replicate the standard dict API, including its constructor. The only additional attribute provided by Items is:


A dictionary containing all declared fields for this Item, not only those populated. The keys are the field names and the values are the Field objects used in the Item declaration.

Field objects

class scrapy.item.Field([arg])

The Field class is just an alias to the built-in dict class and doesn’t provide any extra functionality or attributes. In other words, Field objects are plain-old Python dicts. A separate class is used to support the item declaration syntax based on class attributes.

Item Loaders

Item Loaders provide a convenient mechanism for populating scraped Items. Even though Items can be populated using their own dictionary-like API, Item Loaders provide a much more convenient API for populating them from a scraping process, by automating some common tasks like parsing the raw extracted data before assigning it.

In other words, Items provide the container of scraped data, while Item Loaders provide the mechanism for populating that container.

Item Loaders are designed to provide a flexible, efficient and easy mechanism for extending and overriding different field parsing rules, either by spider, or by source format (HTML, XML, etc) without becoming a nightmare to maintain.

Using Item Loaders to populate items

To use an Item Loader, you must first instantiate it. You can either instantiate it with a dict-like object (e.g. Item or dict) or without one, in which case an Item is automatically instantiated in the Item Loader constructor using the Item class specified in the ItemLoader.default_item_class attribute.

Then, you start collecting values into the Item Loader, typically using Selectors. You can add more than one value to the same item field; the Item Loader will know how to “join” those values later using a proper processing function.

Here is a typical Item Loader usage in a Spider, using the Product item declared in the Items chapter:

from scrapy.loader import ItemLoader
from myproject.items import Product

def parse(self, response):
    l = ItemLoader(item=Product(), response=response)
    l.add_xpath('name', '//div[@class="product_name"]')
    l.add_xpath('name', '//div[@class="product_title"]')
    l.add_xpath('price', '//p[@id="price"]')
    l.add_css('stock', 'p#stock]')
    l.add_value('last_updated', 'today') # you can also use literal values
    return l.load_item()

By quickly looking at that code, we can see the name field is being extracted from two different XPath locations in the page:

  1. //div[@class="product_name"]
  2. //div[@class="product_title"]

In other words, data is being collected by extracting it from two XPath locations, using the add_xpath() method. This is the data that will be assigned to the name field later.

Afterwards, similar calls are used for price and stock fields (the latter using a CSS selector with the add_css() method), and finally the last_update field is populated directly with a literal value (today) using a different method: add_value().

Finally, when all data is collected, the ItemLoader.load_item() method is called which actually returns the item populated with the data previously extracted and collected with the add_xpath(), add_css(), and add_value() calls.

Input and Output processors

An Item Loader contains one input processor and one output processor for each (item) field. The input processor processes the extracted data as soon as it’s received (through the add_xpath(), add_css() or add_value() methods) and the result of the input processor is collected and kept inside the ItemLoader. After collecting all data, the ItemLoader.load_item() method is called to populate and get the populated Item object. That’s when the output processor is called with the data previously collected (and processed using the input processor). The result of the output processor is the final value that gets assigned to the item.

Let’s see an example to illustrate how the input and output processors are called for a particular field (the same applies for any other field):

l = ItemLoader(Product(), some_selector)
l.add_xpath('name', xpath1) # (1)
l.add_xpath('name', xpath2) # (2)
l.add_css('name', css) # (3)
l.add_value('name', 'test') # (4)
return l.load_item() # (5)

So what happens is:

  1. Data from xpath1 is extracted, and passed through the input processor of the name field. The result of the input processor is collected and kept in the Item Loader (but not yet assigned to the item).
  2. Data from xpath2 is extracted, and passed through the same input processor used in (1). The result of the input processor is appended to the data collected in (1) (if any).
  3. This case is similar to the previous ones, except that the data is extracted from the css CSS selector, and passed through the same input processor used in (1) and (2). The result of the input processor is appended to the data collected in (1) and (2) (if any).
  4. This case is also similar to the previous ones, except that the value to be collected is assigned directly, instead of being extracted from a XPath expression or a CSS selector. However, the value is still passed through the input processors. In this case, since the value is not iterable it is converted to an iterable of a single element before passing it to the input processor, because input processor always receive iterables.
  5. The data collected in steps (1), (2), (3) and (4) is passed through the output processor of the name field. The result of the output processor is the value assigned to the name field in the item.

It’s worth noticing that processors are just callable objects, which are called with the data to be parsed, and return a parsed value. So you can use any function as input or output processor. The only requirement is that they must accept one (and only one) positional argument, which will be an iterator.


Both input and output processors must receive an iterator as their first argument. The output of those functions can be anything. The result of input processors will be appended to an internal list (in the Loader) containing the collected values (for that field). The result of the output processors is the value that will be finally assigned to the item.

The other thing you need to keep in mind is that the values returned by input processors are collected internally (in lists) and then passed to output processors to populate the fields.

Last, but not least, Scrapy comes with some commonly used processors built-in for convenience.

Declaring Item Loaders

Item Loaders are declared like Items, by using a class definition syntax. Here is an example:

from scrapy.loader import ItemLoader
from scrapy.loader.processors import TakeFirst, MapCompose, Join

class ProductLoader(ItemLoader):

    default_output_processor = TakeFirst()

    name_in = MapCompose(unicode.title)
    name_out = Join()

    price_in = MapCompose(unicode.strip)

    # ...

As you can see, input processors are declared using the _in suffix while output processors are declared using the _out suffix. And you can also declare a default input/output processors using the ItemLoader.default_input_processor and ItemLoader.default_output_processor attributes.

Declaring Input and Output Processors

As seen in the previous section, input and output processors can be declared in the Item Loader definition, and it’s very common to declare input processors this way. However, there is one more place where you can specify the input and output processors to use: in the Item Field metadata. Here is an example:

import scrapy
from scrapy.loader.processors import Join, MapCompose, TakeFirst
from w3lib.html import remove_tags

def filter_price(value):
    if value.isdigit():
        return value

class Product(scrapy.Item):
    name = scrapy.Field(
    price = scrapy.Field(
        input_processor=MapCompose(remove_tags, filter_price),
>>> from scrapy.loader import ItemLoader
>>> il = ItemLoader(item=Product())
>>> il.add_value('name', [u'Welcome to my', u'<strong>website</strong>'])
>>> il.add_value('price', [u'&euro;', u'<span>1000</span>'])
>>> il.load_item()
{'name': u'Welcome to my website', 'price': u'1000'}

The precedence order, for both input and output processors, is as follows:

  1. Item Loader field-specific attributes: field_in and field_out (most precedence)
  2. Field metadata (input_processor and output_processor key)
  3. Item Loader defaults: ItemLoader.default_input_processor() and ItemLoader.default_output_processor() (least precedence)

See also: Reusing and extending Item Loaders.

Item Loader Context

The Item Loader Context is a dict of arbitrary key/values which is shared among all input and output processors in the Item Loader. It can be passed when declaring, instantiating or using Item Loader. They are used to modify the behaviour of the input/output processors.

For example, suppose you have a function parse_length which receives a text value and extracts a length from it:

def parse_length(text, loader_context):
    unit = loader_context.get('unit', 'm')
    # ... length parsing code goes here ...
    return parsed_length

By accepting a loader_context argument the function is explicitly telling the Item Loader that it’s able to receive an Item Loader context, so the Item Loader passes the currently active context when calling it, and the processor function (parse_length in this case) can thus use them.

There are several ways to modify Item Loader context values:

  1. By modifying the currently active Item Loader context (context attribute):

    loader = ItemLoader(product)
    loader.context['unit'] = 'cm'
  2. On Item Loader instantiation (the keyword arguments of Item Loader constructor are stored in the Item Loader context):

    loader = ItemLoader(product, unit='cm')
  3. On Item Loader declaration, for those input/output processors that support instantiating them with an Item Loader context. MapCompose is one of them:

    class ProductLoader(ItemLoader):
        length_out = MapCompose(parse_length, unit='cm')

ItemLoader objects

class scrapy.loader.ItemLoader([item, selector, response, ]**kwargs)

Return a new Item Loader for populating the given Item. If no item is given, one is instantiated automatically using the class in default_item_class.

When instantiated with a selector or a response parameters the ItemLoader class provides convenient mechanisms for extracting data from web pages using selectors.


The item, selector, response and the remaining keyword arguments are assigned to the Loader context (accessible through the context attribute).

ItemLoader instances have the following methods:

get_value(value, *processors, **kwargs)

Process the given value by the given processors and keyword arguments.

Available keyword arguments:

Parameters:re (str or compiled regex) – a regular expression to use for extracting data from the given value using extract_regex() method, applied before processors


>>> from scrapy.loader.processors import TakeFirst
>>> loader.get_value(u'name: foo', TakeFirst(), unicode.upper, re='name: (.+)')
add_value(field_name, value, *processors, **kwargs)

Process and then add the given value for the given field.

The value is first passed through get_value() by giving the processors and kwargs, and then passed through the field input processor and its result appended to the data collected for that field. If the field already contains collected data, the new data is added.

The given field_name can be None, in which case values for multiple fields may be added. And the processed value should be a dict with field_name mapped to values.


loader.add_value('name', u'Color TV')
loader.add_value('colours', [u'white', u'blue'])
loader.add_value('length', u'100')
loader.add_value('name', u'name: foo', TakeFirst(), re='name: (.+)')
loader.add_value(None, {'name': u'foo', 'sex': u'male'})
replace_value(field_name, value, *processors, **kwargs)

Similar to add_value() but replaces the collected data with the new value instead of adding it.

get_xpath(xpath, *processors, **kwargs)

Similar to ItemLoader.get_value() but receives an XPath instead of a value, which is used to extract a list of unicode strings from the selector associated with this ItemLoader.

  • xpath (str) – the XPath to extract data from
  • re (str or compiled regex) – a regular expression to use for extracting data from the selected XPath region


# HTML snippet: <p class="product-name">Color TV</p>
# HTML snippet: <p id="price">the price is $1200</p>
loader.get_xpath('//p[@id="price"]', TakeFirst(), re='the price is (.*)')
add_xpath(field_name, xpath, *processors, **kwargs)

Similar to ItemLoader.add_value() but receives an XPath instead of a value, which is used to extract a list of unicode strings from the selector associated with this ItemLoader.

See get_xpath() for kwargs.

Parameters:xpath (str) – the XPath to extract data from


# HTML snippet: <p class="product-name">Color TV</p>
loader.add_xpath('name', '//p[@class="product-name"]')
# HTML snippet: <p id="price">the price is $1200</p>
loader.add_xpath('price', '//p[@id="price"]', re='the price is (.*)')
replace_xpath(field_name, xpath, *processors, **kwargs)

Similar to add_xpath() but replaces collected data instead of adding it.

get_css(css, *processors, **kwargs)

Similar to ItemLoader.get_value() but receives a CSS selector instead of a value, which is used to extract a list of unicode strings from the selector associated with this ItemLoader.

  • css (str) – the CSS selector to extract data from
  • re (str or compiled regex) – a regular expression to use for extracting data from the selected CSS region


# HTML snippet: <p class="product-name">Color TV</p>
# HTML snippet: <p id="price">the price is $1200</p>
loader.get_css('p#price', TakeFirst(), re='the price is (.*)')
add_css(field_name, css, *processors, **kwargs)

Similar to ItemLoader.add_value() but receives a CSS selector instead of a value, which is used to extract a list of unicode strings from the selector associated with this ItemLoader.

See get_css() for kwargs.

Parameters:css (str) – the CSS selector to extract data from


# HTML snippet: <p class="product-name">Color TV</p>
loader.add_css('name', 'p.product-name')
# HTML snippet: <p id="price">the price is $1200</p>
loader.add_css('price', 'p#price', re='the price is (.*)')
replace_css(field_name, css, *processors, **kwargs)

Similar to add_css() but replaces collected data instead of adding it.


Populate the item with the data collected so far, and return it. The data collected is first passed through the output processors to get the final value to assign to each item field.


Create a nested loader with an xpath selector. The supplied selector is applied relative to selector associated with this ItemLoader. The nested loader shares the Item with the parent ItemLoader so calls to add_xpath(), add_value(), replace_value(), etc. will behave as expected.


Create a nested loader with a css selector. The supplied selector is applied relative to selector associated with this ItemLoader. The nested loader shares the Item with the parent ItemLoader so calls to add_xpath(), add_value(), replace_value(), etc. will behave as expected.


Return the collected values for the given field.


Return the collected values parsed using the output processor, for the given field. This method doesn’t populate or modify the item at all.


Return the input processor for the given field.


Return the output processor for the given field.

ItemLoader instances have the following attributes:


The Item object being parsed by this Item Loader.


The currently active Context of this Item Loader.


An Item class (or factory), used to instantiate items when not given in the constructor.


The default input processor to use for those fields which don’t specify one.


The default output processor to use for those fields which don’t specify one.


The class used to construct the selector of this ItemLoader, if only a response is given in the constructor. If a selector is given in the constructor this attribute is ignored. This attribute is sometimes overridden in subclasses.


The Selector object to extract data from. It’s either the selector given in the constructor or one created from the response given in the constructor using the default_selector_class. This attribute is meant to be read-only.

Nested Loaders

When parsing related values from a subsection of a document, it can be useful to create nested loaders. Imagine you’re extracting details from a footer of a page that looks something like:


    <a class="social" href="">Like Us</a>
    <a class="social" href="">Follow Us</a>
    <a class="email" href="">Email Us</a>

Without nested loaders, you need to specify the full xpath (or css) for each value that you wish to extract.


loader = ItemLoader(item=Item())
# load stuff not in the footer
loader.add_xpath('social', '//footer/a[@class = "social"]/@href')
loader.add_xpath('email', '//footer/a[@class = "email"]/@href')

Instead, you can create a nested loader with the footer selector and add values relative to the footer. The functionality is the same but you avoid repeating the footer selector.


loader = ItemLoader(item=Item())
# load stuff not in the footer
footer_loader = loader.nested_xpath('//footer')
footer_loader.add_xpath('social', 'a[@class = "social"]/@href')
footer_loader.add_xpath('email', 'a[@class = "email"]/@href')
# no need to call footer_loader.load_item()

You can nest loaders arbitrarily and they work with either xpath or css selectors. As a general guideline, use nested loaders when they make your code simpler but do not go overboard with nesting or your parser can become difficult to read.

Reusing and extending Item Loaders

As your project grows bigger and acquires more and more spiders, maintenance becomes a fundamental problem, especially when you have to deal with many different parsing rules for each spider, having a lot of exceptions, but also wanting to reuse the common processors.

Item Loaders are designed to ease the maintenance burden of parsing rules, without losing flexibility and, at the same time, providing a convenient mechanism for extending and overriding them. For this reason Item Loaders support traditional Python class inheritance for dealing with differences of specific spiders (or groups of spiders).

Suppose, for example, that some particular site encloses their product names in three dashes (e.g. ---Plasma TV---) and you don’t want to end up scraping those dashes in the final product names.

Here’s how you can remove those dashes by reusing and extending the default Product Item Loader (ProductLoader):

from scrapy.loader.processors import MapCompose
from myproject.ItemLoaders import ProductLoader

def strip_dashes(x):
    return x.strip('-')

class SiteSpecificLoader(ProductLoader):
    name_in = MapCompose(strip_dashes, ProductLoader.name_in)

Another case where extending Item Loaders can be very helpful is when you have multiple source formats, for example XML and HTML. In the XML version you may want to remove CDATA occurrences. Here’s an example of how to do it:

from scrapy.loader.processors import MapCompose
from myproject.ItemLoaders import ProductLoader
from myproject.utils.xml import remove_cdata

class XmlProductLoader(ProductLoader):
    name_in = MapCompose(remove_cdata, ProductLoader.name_in)

And that’s how you typically extend input processors.

As for output processors, it is more common to declare them in the field metadata, as they usually depend only on the field and not on each specific site parsing rule (as input processors do). See also: Declaring Input and Output Processors.

There are many other possible ways to extend, inherit and override your Item Loaders, and different Item Loaders hierarchies may fit better for different projects. Scrapy only provides the mechanism; it doesn’t impose any specific organization of your Loaders collection - that’s up to you and your project’s needs.

Available built-in processors

Even though you can use any callable function as input and output processors, Scrapy provides some commonly used processors, which are described below. Some of them, like the MapCompose (which is typically used as input processor) compose the output of several functions executed in order, to produce the final parsed value.

Here is a list of all built-in processors:

class scrapy.loader.processors.Identity

The simplest processor, which doesn’t do anything. It returns the original values unchanged. It doesn’t receive any constructor arguments, nor does it accept Loader contexts.


>>> from scrapy.loader.processors import Identity
>>> proc = Identity()
>>> proc(['one', 'two', 'three'])
['one', 'two', 'three']
class scrapy.loader.processors.TakeFirst

Returns the first non-null/non-empty value from the values received, so it’s typically used as an output processor to single-valued fields. It doesn’t receive any constructor arguments, nor does it accept Loader contexts.


>>> from scrapy.loader.processors import TakeFirst
>>> proc = TakeFirst()
>>> proc(['', 'one', 'two', 'three'])
class scrapy.loader.processors.Join(separator=u' ')

Returns the values joined with the separator given in the constructor, which defaults to u' '. It doesn’t accept Loader contexts.

When using the default separator, this processor is equivalent to the function: u' '.join


>>> from scrapy.loader.processors import Join
>>> proc = Join()
>>> proc(['one', 'two', 'three'])
u'one two three'
>>> proc = Join('<br>')
>>> proc(['one', 'two', 'three'])
class scrapy.loader.processors.Compose(*functions, **default_loader_context)

A processor which is constructed from the composition of the given functions. This means that each input value of this processor is passed to the first function, and the result of that function is passed to the second function, and so on, until the last function returns the output value of this processor.

By default, stop process on None value. This behaviour can be changed by passing keyword argument stop_on_none=False.


>>> from scrapy.loader.processors import Compose
>>> proc = Compose(lambda v: v[0], str.upper)
>>> proc(['hello', 'world'])

Each function can optionally receive a loader_context parameter. For those which do, this processor will pass the currently active Loader context through that parameter.

The keyword arguments passed in the constructor are used as the default Loader context values passed to each function call. However, the final Loader context values passed to functions are overridden with the currently active Loader context accessible through the ItemLoader.context() attribute.

class scrapy.loader.processors.MapCompose(*functions, **default_loader_context)

A processor which is constructed from the composition of the given functions, similar to the Compose processor. The difference with this processor is the way internal results are passed among functions, which is as follows:

The input value of this processor is iterated and the first function is applied to each element. The results of these function calls (one for each element) are concatenated to construct a new iterable, which is then used to apply the second function, and so on, until the last function is applied to each value of the list of values collected so far. The output values of the last function are concatenated together to produce the output of this processor.

Each particular function can return a value or a list of values, which is flattened with the list of values returned by the same function applied to the other input values. The functions can also return None in which case the output of that function is ignored for further processing over the chain.

This processor provides a convenient way to compose functions that only work with single values (instead of iterables). For this reason the MapCompose processor is typically used as input processor, since data is often extracted using the extract() method of selectors, which returns a list of unicode strings.

The example below should clarify how it works:

>>> def filter_world(x):
...     return None if x == 'world' else x
>>> from scrapy.loader.processors import MapCompose
>>> proc = MapCompose(filter_world, unicode.upper)
>>> proc([u'hello', u'world', u'this', u'is', u'scrapy'])
[u'HELLO, u'THIS', u'IS', u'SCRAPY']

As with the Compose processor, functions can receive Loader contexts, and constructor keyword arguments are used as default context values. See Compose processor for more info.

class scrapy.loader.processors.SelectJmes(json_path)

Queries the value using the json path provided to the constructor and returns the output. Requires jmespath ( to run. This processor takes only one input at a time.


>>> from scrapy.loader.processors import SelectJmes, Compose, MapCompose
>>> proc = SelectJmes("foo") #for direct use on lists and dictionaries
>>> proc({'foo': 'bar'})
>>> proc({'foo': {'bar': 'baz'}})
{'bar': 'baz'}

Working with Json:

>>> import json
>>> proc_single_json_str = Compose(json.loads, SelectJmes("foo"))
>>> proc_single_json_str('{"foo": "bar"}')
>>> proc_json_list = Compose(json.loads, MapCompose(SelectJmes('foo')))
>>> proc_json_list('[{"foo":"bar"}, {"baz":"tar"}]')

Scrapy shell

The Scrapy shell is an interactive shell where you can try and debug your scraping code very quickly, without having to run the spider. It’s meant to be used for testing data extraction code, but you can actually use it for testing any kind of code as it is also a regular Python shell.

The shell is used for testing XPath or CSS expressions and see how they work and what data they extract from the web pages you’re trying to scrape. It allows you to interactively test your expressions while you’re writing your spider, without having to run the spider to test every change.

Once you get familiarized with the Scrapy shell, you’ll see that it’s an invaluable tool for developing and debugging your spiders.

Configuring the shell

If you have IPython installed, the Scrapy shell will use it (instead of the standard Python console). The IPython console is much more powerful and provides smart auto-completion and colorized output, among other things.

We highly recommend you install IPython, specially if you’re working on Unix systems (where IPython excels). See the IPython installation guide for more info.

Scrapy also has support for bpython, and will try to use it where IPython is unavailable.

Through scrapy’s settings you can configure it to use any one of ipython, bpython or the standard python shell, regardless of which are installed. This is done by setting the SCRAPY_PYTHON_SHELL environment variable; or by defining it in your scrapy.cfg:

shell = bpython

Launch the shell

To launch the Scrapy shell you can use the shell command like this:

scrapy shell <url>

Where the <url> is the URL you want to scrape.

shell also works for local files. This can be handy if you want to play around with a local copy of a web page. shell understands the following syntaxes for local files:

# UNIX-style
scrapy shell ./path/to/file.html
scrapy shell ../other/path/to/file.html
scrapy shell /absolute/path/to/file.html

# File URI
scrapy shell file:///absolute/path/to/file.html


When using relative file paths, be explicit and prepend them with ./ (or ../ when relevant). scrapy shell index.html will not work as one might expect (and this is by design, not a bug).

Because shell favors HTTP URLs over File URIs, and index.html being syntactically similar to, shell will treat index.html as a domain name and trigger a DNS lookup error:

$ scrapy shell index.html
[ ... scrapy shell starts ... ]
[ ... traceback ... ]
twisted.internet.error.DNSLookupError: DNS lookup failed:
address 'index.html' not found: [Errno -5] No address associated with hostname.

shell will not test beforehand if a file called index.html exists in the current directory. Again, be explicit.

Using the shell

The Scrapy shell is just a regular Python console (or IPython console if you have it available) which provides some additional shortcut functions for convenience.

Available Shortcuts
  • shelp() - print a help with the list of available objects and shortcuts
  • fetch(url[, redirect=True]) - fetch a new response from the given URL and update all related objects accordingly. You can optionaly ask for HTTP 3xx redirections to not be followed by passing redirect=False
  • fetch(request) - fetch a new response from the given request and update all related objects accordingly.
  • view(response) - open the given response in your local web browser, for inspection. This will add a <base> tag to the response body in order for external links (such as images and style sheets) to display properly. Note, however, that this will create a temporary file in your computer, which won’t be removed automatically.
Available Scrapy objects

The Scrapy shell automatically creates some convenient objects from the downloaded page, like the Response object and the Selector objects (for both HTML and XML content).

Those objects are:

  • crawler - the current Crawler object.
  • spider - the Spider which is known to handle the URL, or a Spider object if there is no spider found for the current URL
  • request - a Request object of the last fetched page. You can modify this request using replace() or fetch a new request (without leaving the shell) using the fetch shortcut.
  • response - a Response object containing the last fetched page
  • settings - the current Scrapy settings

Example of shell session

Here’s an example of a typical shell session where we start by scraping the page, and then proceed to scrape the page. Finally, we modify the (Reddit) request method to POST and re-fetch it getting an error. We end the session by typing Ctrl-D (in Unix systems) or Ctrl-Z in Windows.

Keep in mind that the data extracted here may not be the same when you try it, as those pages are not static and could have changed by the time you test this. The only purpose of this example is to get you familiarized with how the Scrapy shell works.

First, we launch the shell:

scrapy shell '' --nolog

Then, the shell fetches the URL (using the Scrapy downloader) and prints the list of available objects and useful shortcuts (you’ll notice that these lines all start with the [s] prefix):

[s] Available Scrapy objects:
[s]   scrapy     scrapy module (contains scrapy.Request, scrapy.Selector, etc)
[s]   crawler    <scrapy.crawler.Crawler object at 0x7f07395dd690>
[s]   item       {}
[s]   request    <GET>
[s]   response   <200>
[s]   settings   <scrapy.settings.Settings object at 0x7f07395dd710>
[s]   spider     <DefaultSpider 'default' at 0x7f0735891690>
[s] Useful shortcuts:
[s]   fetch(url[, redirect=True]) Fetch URL and update local objects (by default, redirects are followed)
[s]   fetch(req)                  Fetch a scrapy.Request and update local objects
[s]   shelp()           Shell help (print this help)
[s]   view(response)    View response in a browser


After that, we can start playing with the objects:

>>> response.xpath('//title/text()').extract_first()
'Scrapy | A Fast and Powerful Scraping and Web Crawling Framework'

>>> fetch("")

>>> response.xpath('//title/text()').extract()
['reddit: the front page of the internet']

>>> request = request.replace(method="POST")

>>> fetch(request)

>>> response.status

>>> from pprint import pprint

>>> pprint(response.headers)
{'Accept-Ranges': ['bytes'],
 'Cache-Control': ['max-age=0, must-revalidate'],
 'Content-Type': ['text/html; charset=UTF-8'],
 'Date': ['Thu, 08 Dec 2016 16:21:19 GMT'],
 'Server': ['snooserv'],
 'Set-Cookie': ['loid=KqNLou0V9SKMX4qb4n;; Max-Age=63071999; Path=/; expires=Sat, 08-Dec-2018 16:21:19 GMT; secure',
                'loidcreated=2016-12-08T16%3A21%3A19.445Z;; Max-Age=63071999; Path=/; expires=Sat, 08-Dec-2018 16:21:19 GMT; secure',
                'loid=vi0ZVe4NkxNWdlH7r7;; Max-Age=63071999; Path=/; expires=Sat, 08-Dec-2018 16:21:19 GMT; secure',
                'loidcreated=2016-12-08T16%3A21%3A19.459Z;; Max-Age=63071999; Path=/; expires=Sat, 08-Dec-2018 16:21:19 GMT; secure'],
 'Vary': ['accept-encoding'],
 'Via': ['1.1 varnish'],
 'X-Cache': ['MISS'],
 'X-Cache-Hits': ['0'],
 'X-Content-Type-Options': ['nosniff'],
 'X-Frame-Options': ['SAMEORIGIN'],
 'X-Moose': ['majestic'],
 'X-Served-By': ['cache-cdg8730-CDG'],
 'X-Timer': ['S1481214079.394283,VS0,VE159'],
 'X-Ua-Compatible': ['IE=edge'],
 'X-Xss-Protection': ['1; mode=block']}

Invoking the shell from spiders to inspect responses

Sometimes you want to inspect the responses that are being processed in a certain point of your spider, if only to check that response you expect is getting there.

This can be achieved by using the function.

Here’s an example of how you would call it from your spider:

import scrapy

class MySpider(scrapy.Spider):
    name = "myspider"
    start_urls = [

    def parse(self, response):
        # We want to inspect one specific response.
        if ".org" in response.url:
            from import inspect_response
            inspect_response(response, self)

        # Rest of parsing code.

When you run the spider, you will get something similar to this:

2014-01-23 17:48:31-0400 [scrapy.core.engine] DEBUG: Crawled (200) <GET> (referer: None)
2014-01-23 17:48:31-0400 [scrapy.core.engine] DEBUG: Crawled (200) <GET> (referer: None)
[s] Available Scrapy objects:
[s]   crawler    <scrapy.crawler.Crawler object at 0x1e16b50>

>>> response.url

Then, you can check if the extraction code is working:

>>> response.xpath('//h1[@class="fn"]')

Nope, it doesn’t. So you can open the response in your web browser and see if it’s the response you were expecting:

>>> view(response)

Finally you hit Ctrl-D (or Ctrl-Z in Windows) to exit the shell and resume the crawling:

>>> ^D
2014-01-23 17:50:03-0400 [scrapy.core.engine] DEBUG: Crawled (200) <GET> (referer: None)

Note that you can’t use the fetch shortcut here since the Scrapy engine is blocked by the shell. However, after you leave the shell, the spider will continue crawling where it stopped, as shown above.

Item Pipeline

After an item has been scraped by a spider, it is sent to the Item Pipeline which processes it through several components that are executed sequentially.

Each item pipeline component (sometimes referred as just “Item Pipeline”) is a Python class that implements a simple method. They receive an item and perform an action over it, also deciding if the item should continue through the pipeline or be dropped and no longer processed.

Typical uses of item pipelines are:

  • cleansing HTML data
  • validating scraped data (checking that the items contain certain fields)
  • checking for duplicates (and dropping them)
  • storing the scraped item in a database

Writing your own item pipeline

Each item pipeline component is a Python class that must implement the following method:

process_item(self, item, spider)

This method is called for every item pipeline component. process_item() must either: return a dict with data, return an Item (or any descendant class) object, return a Twisted Deferred or raise DropItem exception. Dropped items are no longer processed by further pipeline components.

  • item (Item object or a dict) – the item scraped
  • spider (Spider object) – the spider which scraped the item

Additionally, they may also implement the following methods:

open_spider(self, spider)

This method is called when the spider is opened.

Parameters:spider (Spider object) – the spider which was opened
close_spider(self, spider)

This method is called when the spider is closed.

Parameters:spider (Spider object) – the spider which was closed
from_crawler(cls, crawler)

If present, this classmethod is called to create a pipeline instance from a Crawler. It must return a new instance of the pipeline. Crawler object provides access to all Scrapy core components like settings and signals; it is a way for pipeline to access them and hook its functionality into Scrapy.

Parameters:crawler (Crawler object) – crawler that uses this pipeline

Item pipeline example

Price validation and dropping items with no prices

Let’s take a look at the following hypothetical pipeline that adjusts the price attribute for those items that do not include VAT (price_excludes_vat attribute), and drops those items which don’t contain a price:

from scrapy.exceptions import DropItem

class PricePipeline(object):

    vat_factor = 1.15

    def process_item(self, item, spider):
        if item['price']:
            if item['price_excludes_vat']:
                item['price'] = item['price'] * self.vat_factor
            return item
            raise DropItem("Missing price in %s" % item)
Write items to a JSON file

The following pipeline stores all scraped items (from all spiders) into a single items.jl file, containing one item per line serialized in JSON format:

import json

class JsonWriterPipeline(object):

    def open_spider(self, spider):
        self.file = open('items.jl', 'w')

    def close_spider(self, spider):

    def process_item(self, item, spider):
        line = json.dumps(dict(item)) + "\n"
        return item


The purpose of JsonWriterPipeline is just to introduce how to write item pipelines. If you really want to store all scraped items into a JSON file you should use the Feed exports.

Write items to MongoDB

In this example we’ll write items to MongoDB using pymongo. MongoDB address and database name are specified in Scrapy settings; MongoDB collection is named after item class.

The main point of this example is to show how to use from_crawler() method and how to clean up the resources properly.:

import pymongo

class MongoPipeline(object):

    collection_name = 'scrapy_items'

    def __init__(self, mongo_uri, mongo_db):
        self.mongo_uri = mongo_uri
        self.mongo_db = mongo_db

    def from_crawler(cls, crawler):
        return cls(
            mongo_db=crawler.settings.get('MONGO_DATABASE', 'items')

    def open_spider(self, spider):
        self.client = pymongo.MongoClient(self.mongo_uri)
        self.db = self.client[self.mongo_db]

    def close_spider(self, spider):

    def process_item(self, item, spider):
        return item
Take screenshot of item

This example demonstrates how to return Deferred from process_item() method. It uses Splash to render screenshot of item url. Pipeline makes request to locally running instance of Splash. After request is downloaded and Deferred callback fires, it saves item to a file and adds filename to an item.

import scrapy
import hashlib
from urllib.parse import quote

class ScreenshotPipeline(object):
    """Pipeline that uses Splash to render screenshot of
    every Scrapy item."""

    SPLASH_URL = "http://localhost:8050/render.png?url={}"

    def process_item(self, item, spider):
        encoded_item_url = quote(item["url"])
        screenshot_url = self.SPLASH_URL.format(encoded_item_url)
        request = scrapy.Request(screenshot_url)
        dfd =, spider)
        dfd.addBoth(self.return_item, item)
        return dfd

    def return_item(self, response, item):
        if response.status != 200:
            # Error happened, return item.
            return item

        # Save screenshot to file, filename will be hash of url.
        url = item["url"]
        url_hash = hashlib.md5(url.encode("utf8")).hexdigest()
        filename = "{}.png".format(url_hash)
        with open(filename, "wb") as f:

        # Store filename in item.
        item["screenshot_filename"] = filename
        return item
Duplicates filter

A filter that looks for duplicate items, and drops those items that were already processed. Let’s say that our items have a unique id, but our spider returns multiples items with the same id:

from scrapy.exceptions import DropItem

class DuplicatesPipeline(object):

    def __init__(self):
        self.ids_seen = set()

    def process_item(self, item, spider):
        if item['id'] in self.ids_seen:
            raise DropItem("Duplicate item found: %s" % item)
            return item

Activating an Item Pipeline component

To activate an Item Pipeline component you must add its class to the ITEM_PIPELINES setting, like in the following example:

    'myproject.pipelines.PricePipeline': 300,
    'myproject.pipelines.JsonWriterPipeline': 800,

The integer values you assign to classes in this setting determine the order in which they run: items go through from lower valued to higher valued classes. It’s customary to define these numbers in the 0-1000 range.

Feed exports

New in version 0.10.

One of the most frequently required features when implementing scrapers is being able to store the scraped data properly and, quite often, that means generating an “export file” with the scraped data (commonly called “export feed”) to be consumed by other systems.

Scrapy provides this functionality out of the box with the Feed Exports, which allows you to generate a feed with the scraped items, using multiple serialization formats and storage backends.

Serialization formats

For serializing the scraped data, the feed exports use the Item exporters. These formats are supported out of the box:

But you can also extend the supported format through the FEED_EXPORTERS setting.

JSON lines
  • FEED_FORMAT: csv
  • Exporter used: CsvItemExporter
  • To specify columns to export and their order use FEED_EXPORT_FIELDS. Other feed exporters can also use this option, but it is important for CSV because unlike many other export formats CSV uses a fixed header.
  • FEED_FORMAT: marshal
  • Exporter used: MarshalItemExporter


When using the feed exports you define where to store the feed using a URI (through the FEED_URI setting). The feed exports supports multiple storage backend types which are defined by the URI scheme.

The storages backends supported out of the box are:

Some storage backends may be unavailable if the required external libraries are not available. For example, the S3 backend is only available if the botocore or boto library is installed (Scrapy supports boto only on Python 2).

Storage URI parameters

The storage URI can also contain parameters that get replaced when the feed is being created. These parameters are:

  • %(time)s - gets replaced by a timestamp when the feed is being created
  • %(name)s - gets replaced by the spider name

Any other named parameter gets replaced by the spider attribute of the same name. For example, %(site_id)s would get replaced by the spider.site_id attribute the moment the feed is being created.

Here are some examples to illustrate:

  • Store in FTP using one directory per spider:
  • Store in S3 using one directory per spider:
    • s3://mybucket/scraping/feeds/%(name)s/%(time)s.json

Storage backends

Local filesystem

The feeds are stored in the local filesystem.

  • URI scheme: file
  • Example URI: file:///tmp/export.csv
  • Required external libraries: none

Note that for the local filesystem storage (only) you can omit the scheme if you specify an absolute path like /tmp/export.csv. This only works on Unix systems though.


The feeds are stored in a FTP server.

  • URI scheme: ftp
  • Example URI:
  • Required external libraries: none

The feeds are stored on Amazon S3.

  • URI scheme: s3
  • Example URIs:
    • s3://mybucket/path/to/export.csv
    • s3://aws_key:aws_secret@mybucket/path/to/export.csv
  • Required external libraries: botocore or boto

The AWS credentials can be passed as user/password in the URI, or they can be passed through the following settings:

Standard output

The feeds are written to the standard output of the Scrapy process.

  • URI scheme: stdout
  • Example URI: stdout:
  • Required external libraries: none


These are the settings used for configuring the feed exports:


Default: None

The URI of the export feed. See Storage backends for supported URI schemes.

This setting is required for enabling the feed exports.


The serialization format to be used for the feed. See Serialization formats for possible values.


Default: None

The encoding to be used for the feed.

If unset or set to None (default) it uses UTF-8 for everything except JSON output, which uses safe numeric encoding (\uXXXX sequences) for historic reasons.

Use utf-8 if you want UTF-8 for JSON too.


Default: None

A list of fields to export, optional. Example: FEED_EXPORT_FIELDS = ["foo", "bar", "baz"].

Use FEED_EXPORT_FIELDS option to define fields to export and their order.

When FEED_EXPORT_FIELDS is empty or None (default), Scrapy uses fields defined in dicts or Item subclasses a spider is yielding.

If an exporter requires a fixed set of fields (this is the case for CSV export format) and FEED_EXPORT_FIELDS is empty or None, then Scrapy tries to infer field names from the exported data - currently it uses field names from the first item.


Default: 0

Amount of spaces used to indent the output on each level. If FEED_EXPORT_INDENT is a non-negative integer, then array elements and object members will be pretty-printed with that indent level. An indent level of 0 (the default), or negative, will put each item on a new line. None selects the most compact representation.

Currently implemented only by JsonItemExporter and XmlItemExporter, i.e. when you are exporting to .json or .xml.


Default: False

Whether to export empty feeds (ie. feeds with no items).


Default: {}

A dict containing additional feed storage backends supported by your project. The keys are URI schemes and the values are paths to storage classes.



    '': 'scrapy.extensions.feedexport.FileFeedStorage',
    'file': 'scrapy.extensions.feedexport.FileFeedStorage',
    'stdout': 'scrapy.extensions.feedexport.StdoutFeedStorage',
    's3': 'scrapy.extensions.feedexport.S3FeedStorage',
    'ftp': 'scrapy.extensions.feedexport.FTPFeedStorage',

A dict containing the built-in feed storage backends supported by Scrapy. You can disable any of these backends by assigning None to their URI scheme in FEED_STORAGES. E.g., to disable the built-in FTP storage backend (without replacement), place this in your

    'ftp': None,

Default: {}

A dict containing additional exporters supported by your project. The keys are serialization formats and the values are paths to Item exporter classes.



    'json': 'scrapy.exporters.JsonItemExporter',
    'jsonlines': 'scrapy.exporters.JsonLinesItemExporter',
    'jl': 'scrapy.exporters.JsonLinesItemExporter',
    'csv': 'scrapy.exporters.CsvItemExporter',
    'xml': 'scrapy.exporters.XmlItemExporter',
    'marshal': 'scrapy.exporters.MarshalItemExporter',
    'pickle': 'scrapy.exporters.PickleItemExporter',

A dict containing the built-in feed exporters supported by Scrapy. You can disable any of these exporters by assigning None to their serialization format in FEED_EXPORTERS. E.g., to disable the built-in CSV exporter (without replacement), place this in your

    'csv': None,

Requests and Responses

Scrapy uses Request and Response objects for crawling web sites.

Typically, Request objects are generated in the spiders and pass across the system until they reach the Downloader, which executes the request and returns a Response object which travels back to the spider that issued the request.

Both Request and Response classes have subclasses which add functionality not required in the base classes. These are described below in Request subclasses and Response subclasses.

Request objects

class scrapy.http.Request(url[, callback, method='GET', headers, body, cookies, meta, encoding='utf-8', priority=0, dont_filter=False, errback, flags])

A Request object represents an HTTP request, which is usually generated in the Spider and executed by the Downloader, and thus generating a Response.

  • url (string) – the URL of this request
  • callback (callable) – the function that will be called with the response of this request (once its downloaded) as its first parameter. For more information see Passing additional data to callback functions below. If a Request doesn’t specify a callback, the spider’s parse() method will be used. Note that if exceptions are raised during processing, errback is called instead.
  • method (string) – the HTTP method of this request. Defaults to 'GET'.
  • meta (dict) – the initial values for the Request.meta attribute. If given, the dict passed in this parameter will be shallow copied.
  • body (str or unicode) – the request body. If a unicode is passed, then it’s encoded to str using the encoding passed (which defaults to utf-8). If body is not given, an empty string is stored. Regardless of the type of this argument, the final value stored will be a str (never unicode or None).
  • headers (dict) – the headers of this request. The dict values can be strings (for single valued headers) or lists (for multi-valued headers). If None is passed as value, the HTTP header will not be sent at all.
  • cookies (dict or list) –

    the request cookies. These can be sent in two forms.

    1. Using a dict:
      request_with_cookies = Request(url="",
                                     cookies={'currency': 'USD', 'country': 'UY'})
    2. Using a list of dicts:
      request_with_cookies = Request(url="",
                                     cookies=[{'name': 'currency',
                                              'value': 'USD',
                                              'domain': '',
                                              'path': '/currency'}])

    The latter form allows for customizing the domain and path attributes of the cookie. This is only useful if the cookies are saved for later requests.

    When some site returns cookies (in a response) those are stored in the cookies for that domain and will be sent again in future requests. That’s the typical behaviour of any regular web browser. However, if, for some reason, you want to avoid merging with existing cookies you can instruct Scrapy to do so by setting the dont_merge_cookies key to True in the Request.meta.

    Example of request without merging cookies:

    request_with_cookies = Request(url="",
                                   cookies={'currency': 'USD', 'country': 'UY'},
                                   meta={'dont_merge_cookies': True})

    For more info see CookiesMiddleware.

  • encoding (string) – the encoding of this request (defaults to 'utf-8'). This encoding will be used to percent-encode the URL and to convert the body to str (if given as unicode).
  • priority (int) – the priority of this request (defaults to 0). The priority is used by the scheduler to define the order used to process requests. Requests with a higher priority value will execute earlier. Negative values are allowed in order to indicate relatively low-priority.
  • dont_filter (boolean) – indicates that this request should not be filtered by the scheduler. This is used when you want to perform an identical request multiple times, to ignore the duplicates filter. Use it with care, or you will get into crawling loops. Default to False.
  • errback (callable) – a function that will be called if any exception was raised while processing the request. This includes pages that failed with 404 HTTP errors and such. It receives a Twisted Failure instance as first parameter. For more information, see Using errbacks to catch exceptions in request processing below.
  • flags (list) – Flags sent to the request, can be used for logging or similar purposes.

A string containing the URL of this request. Keep in mind that this attribute contains the escaped URL, so it can differ from the URL passed in the constructor.

This attribute is read-only. To change the URL of a Request use replace().


A string representing the HTTP method in the request. This is guaranteed to be uppercase. Example: "GET", "POST", "PUT", etc


A dictionary-like object which contains the request headers.


A str that contains the request body.

This attribute is read-only. To change the body of a Request use replace().


A dict that contains arbitrary metadata for this request. This dict is empty for new Requests, and is usually populated by different Scrapy components (extensions, middlewares, etc). So the data contained in this dict depends on the extensions you have enabled.

See Request.meta special keys for a list of special meta keys recognized by Scrapy.

This dict is shallow copied when the request is cloned using the copy() or replace() methods, and can also be accessed, in your spider, from the response.meta attribute.


Return a new Request which is a copy of this Request. See also: Passing additional data to callback functions.

replace([url, method, headers, body, cookies, meta, encoding, dont_filter, callback, errback])

Return a Request object with the same members, except for those members given new values by whichever keyword arguments are specified. The attribute Request.meta is copied by default (unless a new value is given in the meta argument). See also Passing additional data to callback functions.

Passing additional data to callback functions

The callback of a request is a function that will be called when the response of that request is downloaded. The callback function will be called with the downloaded Response object as its first argument.


def parse_page1(self, response):
    return scrapy.Request("",

def parse_page2(self, response):
    # this would log"Visited %s", response.url)

In some cases you may be interested in passing arguments to those callback functions so you can receive the arguments later, in the second callback. You can use the Request.meta attribute for that.

Here’s an example of how to pass an item using this mechanism, to populate different fields from different pages:

def parse_page1(self, response):
    item = MyItem()
    item['main_url'] = response.url
    request = scrapy.Request("",
    request.meta['item'] = item
    yield request

def parse_page2(self, response):
    item = response.meta['item']
    item['other_url'] = response.url
    yield item
Using errbacks to catch exceptions in request processing

The errback of a request is a function that will be called when an exception is raise while processing it.

It receives a Twisted Failure instance as first parameter and can be used to track connection establishment timeouts, DNS errors etc.

Here’s an example spider logging all errors and catching some specific errors if needed:

import scrapy

from scrapy.spidermiddlewares.httperror import HttpError
from twisted.internet.error import DNSLookupError
from twisted.internet.error import TimeoutError, TCPTimedOutError

class ErrbackSpider(scrapy.Spider):
    name = "errback_example"
    start_urls = [
        "",              # HTTP 200 expected
        "",    # Not found error
        "",    # server issue
        "",        # non-responding host, timeout expected
        "",       # DNS error expected

    def start_requests(self):
        for u in self.start_urls:
            yield scrapy.Request(u, callback=self.parse_httpbin,

    def parse_httpbin(self, response):'Got successful response from {}'.format(response.url))
        # do something useful here...

    def errback_httpbin(self, failure):
        # log all failures

        # in case you want to do something special for some errors,
        # you may need the failure's type:

        if failure.check(HttpError):
            # these exceptions come from HttpError spider middleware
            # you can get the non-200 response
            response = failure.value.response
            self.logger.error('HttpError on %s', response.url)

        elif failure.check(DNSLookupError):
            # this is the original request
            request = failure.request
            self.logger.error('DNSLookupError on %s', request.url)

        elif failure.check(TimeoutError, TCPTimedOutError):
            request = failure.request
            self.logger.error('TimeoutError on %s', request.url)

Request.meta special keys

The Request.meta attribute can contain any arbitrary data, but there are some special keys recognized by Scrapy and its built-in extensions.

Those are:


The IP of the outgoing IP address to use for the performing the request.


The amount of time (in secs) that the downloader will wait before timing out. See also: DOWNLOAD_TIMEOUT.


The amount of time spent to fetch the response, since the request has been started, i.e. HTTP message sent over the network. This meta key only becomes available when the response has been downloaded. While most other meta keys are used to control Scrapy behavior, this one is supposed to be read-only.


Whether or not to fail on broken responses. See: DOWNLOAD_FAIL_ON_DATALOSS.


The meta key is used set retry times per request. When initialized, the max_retry_times meta key takes higher precedence over the RETRY_TIMES setting.

Request subclasses

Here is the list of built-in Request subclasses. You can also subclass it to implement your own custom functionality.

FormRequest objects

The FormRequest class extends the base Request with functionality for dealing with HTML forms. It uses lxml.html forms to pre-populate form fields with form data from Response objects.

class scrapy.http.FormRequest(url[, formdata, ...])

The FormRequest class adds a new argument to the constructor. The remaining arguments are the same as for the Request class and are not documented here.

Parameters:formdata (dict or iterable of tuples) – is a dictionary (or iterable of (key, value) tuples) containing HTML Form data which will be url-encoded and assigned to the body of the request.

The FormRequest objects support the following class method in addition to the standard Request methods:

classmethod from_response(response[, formname=None, formid=None, formnumber=0, formdata=None, formxpath=None, formcss=None, clickdata=None, dont_click=False, ...])

Returns a new FormRequest object with its form field values pre-populated with those found in the HTML <form> element contained in the given response. For an example see Using FormRequest.from_response() to simulate a user login.

The policy is to automatically simulate a click, by default, on any form control that looks clickable, like a <input type="submit">. Even though this is quite convenient, and often the desired behaviour, sometimes it can cause problems which could be hard to debug. For example, when working with forms that are filled and/or submitted using javascript, the default from_response() behaviour may not be the most appropriate. To disable this behaviour you can set the dont_click argument to True. Also, if you want to change the control clicked (instead of disabling it) you can also use the clickdata argument.


Using this method with select elements which have leading or trailing whitespace in the option values will not work due to a bug in lxml, which should be fixed in lxml 3.8 and above.

  • response (Response object) – the response containing a HTML form which will be used to pre-populate the form fields
  • formname (string) – if given, the form with name attribute set to this value will be used.
  • formid (string) – if given, the form with id attribute set to this value will be used.
  • formxpath (string) – if given, the first form that matches the xpath will be used.
  • formcss (string) – if given, the first form that matches the css selector will be used.
  • formnumber (integer) – the number of form to use, when the response contains multiple forms. The first one (and also the default) is 0.
  • formdata (dict) – fields to override in the form data. If a field was already present in the response <form> element, its value is overridden by the one passed in this parameter. If a value passed in this parameter is None, the field will not be included in the request, even if it was present in the response <form> element.
  • clickdata (dict) – attributes to lookup the control clicked. If it’s not given, the form data will be submitted simulating a click on the first clickable element. In addition to html attributes, the control can be identified by its zero-based index relative to other submittable inputs inside the form, via the nr attribute.
  • dont_click (boolean) – If True, the form data will be submitted without clicking in any element.

The other parameters of this class method are passed directly to the FormRequest constructor.

New in version 0.10.3: The formname parameter.

New in version 0.17: The formxpath parameter.

New in version 1.1.0: The formcss parameter.

New in version 1.1.0: The formid parameter.

Request usage examples
Using FormRequest to send data via HTTP POST

If you want to simulate a HTML Form POST in your spider and send a couple of key-value fields, you can return a FormRequest object (from your spider) like this:

return [FormRequest(url="",
                    formdata={'name': 'John Doe', 'age': '27'},
Using FormRequest.from_response() to simulate a user login

It is usual for web sites to provide pre-populated form fields through <input type="hidden"> elements, such as session related data or authentication tokens (for login pages). When scraping, you’ll want these fields to be automatically pre-populated and only override a couple of them, such as the user name and password. You can use the FormRequest.from_response() method for this job. Here’s an example spider which uses it:

import scrapy

class LoginSpider(scrapy.Spider):
    name = ''
    start_urls = ['']

    def parse(self, response):
        return scrapy.FormRequest.from_response(
            formdata={'username': 'john', 'password': 'secret'},

    def after_login(self, response):
        # check login succeed before going on
        if "authentication failed" in response.body:
            self.logger.error("Login failed")

        # continue scraping with authenticated session...

Response objects

class scrapy.http.Response(url[, status=200, headers=None, body=b'', flags=None, request=None])

A Response object represents an HTTP response, which is usually downloaded (by the Downloader) and fed to the Spiders for processing.

  • url (string) – the URL of this response
  • status (integer) – the HTTP status of the response. Defaults to 200.
  • headers (dict) – the headers of this response. The dict values can be strings (for single valued headers) or lists (for multi-valued headers).
  • body (bytes) – the response body. To access the decoded text as str (unicode in Python 2) you can use response.text from an encoding-aware Response subclass, such as TextResponse.
  • flags (list) – is a list containing the initial values for the Response.flags attribute. If given, the list will be shallow copied.
  • request (Request object) – the initial value of the Response.request attribute. This represents the Request that generated this response.

A string containing the URL of the response.

This attribute is read-only. To change the URL of a Response use replace().


An integer representing the HTTP status of the response. Example: 200, 404.


A dictionary-like object which contains the response headers. Values can be accessed using get() to return the first header value with the specified name or getlist() to return all header values with the specified name. For example, this call will give you all cookies in the headers:


The body of this Response. Keep in mind that Response.body is always a bytes object. If you want the unicode version use TextResponse.text (only available in TextResponse and subclasses).

This attribute is read-only. To change the body of a Response use replace().


The Request object that generated this response. This attribute is assigned in the Scrapy engine, after the response and the request have passed through all Downloader Middlewares. In particular, this means that:

  • HTTP redirections will cause the original request (to the URL before redirection) to be assigned to the redirected response (with the final URL after redirection).
  • Response.request.url doesn’t always equal Response.url
  • This attribute is only available in the spider code, and in the Spider Middlewares, but not in Downloader Middlewares (although you have the Request available there by other means) and handlers of the response_downloaded signal.

A shortcut to the Request.meta attribute of the Response.request object (ie. self.request.meta).

Unlike the Response.request attribute, the Response.meta attribute is propagated along redirects and retries, so you will get the original Request.meta sent from your spider.

See also

Request.meta attribute


A list that contains flags for this response. Flags are labels used for tagging Responses. For example: ‘cached’, ‘redirected’, etc. And they’re shown on the string representation of the Response (__str__ method) which is used by the engine for logging.


Returns a new Response which is a copy of this Response.

replace([url, status, headers, body, request, flags, cls])

Returns a Response object with the same members, except for those members given new values by whichever keyword arguments are specified. The attribute Response.meta is copied by default.


Constructs an absolute url by combining the Response’s url with a possible relative url.

This is a wrapper over urlparse.urljoin, it’s merely an alias for making this call:

urlparse.urljoin(response.url, url)
follow(url, callback=None, method='GET', headers=None, body=None, cookies=None, meta=None, encoding='utf-8', priority=0, dont_filter=False, errback=None)

Return a Request instance to follow a link url. It accepts the same arguments as Request.__init__ method, but url can be a relative URL or a object, not only an absolute URL.

TextResponse provides a follow() method which supports selectors in addition to absolute/relative URLs and Link objects.

Response subclasses

Here is the list of available built-in Response subclasses. You can also subclass the Response class to implement your own functionality.

TextResponse objects
class scrapy.http.TextResponse(url[, encoding[, ...]])

TextResponse objects adds encoding capabilities to the base Response class, which is meant to be used only for binary data, such as images, sounds or any media file.

TextResponse objects support a new constructor argument, in addition to the base Response objects. The remaining functionality is the same as for the Response class and is not documented here.

Parameters:encoding (string) – is a string which contains the encoding to use for this response. If you create a TextResponse object with a unicode body, it will be encoded using this encoding (remember the body attribute is always a string). If encoding is None (default value), the encoding will be looked up in the response headers and body instead.

TextResponse objects support the following attributes in addition to the standard Response ones:


Response body, as unicode.

The same as response.body.decode(response.encoding), but the result is cached after the first call, so you can access response.text multiple times without extra overhead.


unicode(response.body) is not a correct way to convert response body to unicode: you would be using the system default encoding (typically ascii) instead of the response encoding.


A string with the encoding of this response. The encoding is resolved by trying the following mechanisms, in order:

  1. the encoding passed in the constructor encoding argument
  2. the encoding declared in the Content-Type HTTP header. If this encoding is not valid (ie. unknown), it is ignored and the next resolution mechanism is tried.
  3. the encoding declared in the response body. The TextResponse class doesn’t provide any special functionality for this. However, the HtmlResponse and XmlResponse classes do.
  4. the encoding inferred by looking at the response body. This is the more fragile method but also the last one tried.

A Selector instance using the response as target. The selector is lazily instantiated on first access.

TextResponse objects support the following methods in addition to the standard Response ones:


A shortcut to TextResponse.selector.xpath(query):


A shortcut to TextResponse.selector.css(query):

follow(url, callback=None, method='GET', headers=None, body=None, cookies=None, meta=None, encoding=None, priority=0, dont_filter=False, errback=None)

Return a Request instance to follow a link url. It accepts the same arguments as Request.__init__ method, but url can be not only an absolute URL, but also

  • a relative URL;
  • a object (e.g. a link extractor result);
  • an attribute Selector (not SelectorList) - e.g. response.css('a::attr(href)')[0] or response.xpath('//img/@src')[0].
  • a Selector for <a> or <link> element, e.g. response.css('a.my_link')[0].

See A shortcut for creating Requests for usage examples.


The same as text, but available as a method. This method is kept for backwards compatibility; please prefer response.text.

HtmlResponse objects
class scrapy.http.HtmlResponse(url[, ...])

The HtmlResponse class is a subclass of TextResponse which adds encoding auto-discovering support by looking into the HTML meta http-equiv attribute. See TextResponse.encoding.

XmlResponse objects
class scrapy.http.XmlResponse(url[, ...])

The XmlResponse class is a subclass of TextResponse which adds encoding auto-discovering support by looking into the XML declaration line. See TextResponse.encoding.


The Scrapy settings allows you to customize the behaviour of all Scrapy components, including the core, extensions, pipelines and spiders themselves.

The infrastructure of the settings provides a global namespace of key-value mappings that the code can use to pull configuration values from. The settings can be populated through different mechanisms, which are described below.

The settings are also the mechanism for selecting the currently active Scrapy project (in case you have many).

For a list of available built-in settings see: Built-in settings reference.

Designating the settings

When you use Scrapy, you have to tell it which settings you’re using. You can do this by using an environment variable, SCRAPY_SETTINGS_MODULE.

The value of SCRAPY_SETTINGS_MODULE should be in Python path syntax, e.g. myproject.settings. Note that the settings module should be on the Python import search path.

Populating the settings

Settings can be populated using different mechanisms, each of which having a different precedence. Here is the list of them in decreasing order of precedence:

  1. Command line options (most precedence)
  2. Settings per-spider
  3. Project settings module
  4. Default settings per-command
  5. Default global settings (less precedence)

The population of these settings sources is taken care of internally, but a manual handling is possible using API calls. See the Settings API topic for reference.

These mechanisms are described in more detail below.

1. Command line options

Arguments provided by the command line are the ones that take most precedence, overriding any other options. You can explicitly override one (or more) settings using the -s (or --set) command line option.


scrapy crawl myspider -s LOG_FILE=scrapy.log
2. Settings per-spider

Spiders (See the Spiders chapter for reference) can define their own settings that will take precedence and override the project ones. They can do so by setting their custom_settings attribute:

class MySpider(scrapy.Spider):
    name = 'myspider'

    custom_settings = {
        'SOME_SETTING': 'some value',
3. Project settings module

The project settings module is the standard configuration file for your Scrapy project, it’s where most of your custom settings will be populated. For a standard Scrapy project, this means you’ll be adding or changing the settings in the file created for your project.

4. Default settings per-command

Each Scrapy tool command can have its own default settings, which override the global default settings. Those custom command settings are specified in the default_settings attribute of the command class.

5. Default global settings

The global defaults are located in the scrapy.settings.default_settings module and documented in the Built-in settings reference section.

How to access settings

In a spider, the settings are available through self.settings:

class MySpider(scrapy.Spider):
    name = 'myspider'
    start_urls = ['']

    def parse(self, response):
        print("Existing settings: %s" % self.settings.attributes.keys())


The settings attribute is set in the base Spider class after the spider is initialized. If you want to use the settings before the initialization (e.g., in your spider’s __init__() method), you’ll need to override the from_crawler() method.

Settings can be accessed through the scrapy.crawler.Crawler.settings attribute of the Crawler that is passed to from_crawler method in extensions, middlewares and item pipelines:

class MyExtension(object):
    def __init__(self, log_is_enabled=False):
        if log_is_enabled:
            print("log is enabled!")

    def from_crawler(cls, crawler):
        settings = crawler.settings
        return cls(settings.getbool('LOG_ENABLED'))

The settings object can be used like a dict (e.g., settings['LOG_ENABLED']), but it’s usually preferred to extract the setting in the format you need it to avoid type errors, using one of the methods provided by the Settings API.

Rationale for setting names

Setting names are usually prefixed with the component that they configure. For example, proper setting names for a fictional robots.txt extension would be ROBOTSTXT_ENABLED, ROBOTSTXT_OBEY, ROBOTSTXT_CACHEDIR, etc.

Built-in settings reference

Here’s a list of all available Scrapy settings, in alphabetical order, along with their default values and the scope where they apply.

The scope, where available, shows where the setting is being used, if it’s tied to any particular component. In that case the module of that component will be shown, typically an extension, middleware or pipeline. It also means that the component must be enabled in order for the setting to have any effect.


Default: None

The AWS access key used by code that requires access to Amazon Web services, such as the S3 feed storage backend.


Default: None

The AWS secret key used by code that requires access to Amazon Web services, such as the S3 feed storage backend.


Default: 'scrapybot'

The name of the bot implemented by this Scrapy project (also known as the project name). This will be used to construct the User-Agent by default, and also for logging.

It’s automatically populated with your project name when you create your project with the startproject command.


Default: 100

Maximum number of concurrent items (per response) to process in parallel in the Item Processor (also known as the Item Pipeline).


Default: 16

The maximum number of concurrent (ie. simultaneous) requests that will be performed by the Scrapy downloader.


Default: 8

The maximum number of concurrent (ie. simultaneous) requests that will be performed to any single domain.

See also: AutoThrottle extension and its AUTOTHROTTLE_TARGET_CONCURRENCY option.


Default: 0

The maximum number of concurrent (ie. simultaneous) requests that will be performed to any single IP. If non-zero, the CONCURRENT_REQUESTS_PER_DOMAIN setting is ignored, and this one is used instead. In other words, concurrency limits will be applied per IP, not per domain.

This setting also affects DOWNLOAD_DELAY and AutoThrottle extension: if CONCURRENT_REQUESTS_PER_IP is non-zero, download delay is enforced per IP, not per domain.


Default: 'scrapy.item.Item'

The default class that will be used for instantiating items in the the Scrapy shell.



    'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
    'Accept-Language': 'en',

The default headers used for Scrapy HTTP Requests. They’re populated in the DefaultHeadersMiddleware.


Default: 0

Scope: scrapy.spidermiddlewares.depth.DepthMiddleware

The maximum depth that will be allowed to crawl for any site. If zero, no limit will be imposed.


Default: 0

Scope: scrapy.spidermiddlewares.depth.DepthMiddleware

An integer that is used to adjust the request priority based on its depth:

  • if zero (default), no priority adjustment is made from depth
  • a positive value will decrease the priority, i.e. higher depth requests will be processed later ; this is commonly used when doing breadth-first crawls (BFO)
  • a negative value will increase priority, i.e., higher depth requests will be processed sooner (DFO)

See also: Does Scrapy crawl in breadth-first or depth-first order? about tuning Scrapy for BFO or DFO.


This setting adjusts priority in the opposite way compared to other priority settings REDIRECT_PRIORITY_ADJUST and RETRY_PRIORITY_ADJUST.


Default: True

Scope: scrapy.spidermiddlewares.depth.DepthMiddleware

Whether to collect maximum depth stats.


Default: False

Scope: scrapy.spidermiddlewares.depth.DepthMiddleware

Whether to collect verbose depth stats. If this is enabled, the number of requests for each depth is collected in the stats.


Default: True

Whether to enable DNS in-memory cache.


Default: 10000

DNS in-memory cache size.


Default: 60

Timeout for processing of DNS queries in seconds. Float is supported.


Default: 'scrapy.core.downloader.Downloader'

The downloader to use for crawling.


Default: 'scrapy.core.downloader.webclient.ScrapyHTTPClientFactory'

Defines a Twisted protocol.ClientFactory class to use for HTTP/1.0 connections (for HTTP10DownloadHandler).


HTTP/1.0 is rarely used nowadays so you can safely ignore this setting, unless you use Twisted<11.1, or if you really want to use HTTP/1.0 and override DOWNLOAD_HANDLERS_BASE for http(s) scheme accordingly, i.e. to 'scrapy.core.downloader.handlers.http.HTTP10DownloadHandler'.


Default: 'scrapy.core.downloader.contextfactory.ScrapyClientContextFactory'

Represents the classpath to the ContextFactory to use.

Here, “ContextFactory” is a Twisted term for SSL/TLS contexts, defining the TLS/SSL protocol version to use, whether to do certificate verification, or even enable client-side authentication (and various other things).


Scrapy default context factory does NOT perform remote server certificate verification. This is usually fine for web scraping.

If you do need remote server certificate verification enabled, Scrapy also has another context factory class that you can set, 'scrapy.core.downloader.contextfactory.BrowserLikeContextFactory', which uses the platform’s certificates to validate remote endpoints. This is only available if you use Twisted>=14.0.

If you do use a custom ContextFactory, make sure it accepts a method parameter at init (this is the OpenSSL.SSL method mapping DOWNLOADER_CLIENT_TLS_METHOD).


Default: 'TLS'

Use this setting to customize the TLS/SSL method used by the default HTTP/1.1 downloader.

This setting must be one of these string values:

  • 'TLS': maps to OpenSSL’s TLS_method() (a.k.a SSLv23_method()), which allows protocol negotiation, starting from the highest supported by the platform; default, recommended
  • 'TLSv1.0': this value forces HTTPS connections to use TLS version 1.0 ; set this if you want the behavior of Scrapy<1.1
  • 'TLSv1.1': forces TLS version 1.1
  • 'TLSv1.2': forces TLS version 1.2
  • 'SSLv3': forces SSL version 3 (not recommended)


We recommend that you use PyOpenSSL>=0.13 and Twisted>=0.13 or above (Twisted>=14.0 if you can).


Default:: {}

A dict containing the downloader middlewares enabled in your project, and their orders. For more info see Activating a downloader middleware.



    'scrapy.downloadermiddlewares.robotstxt.RobotsTxtMiddleware': 100,
    'scrapy.downloadermiddlewares.httpauth.HttpAuthMiddleware': 300,
    'scrapy.downloadermiddlewares.downloadtimeout.DownloadTimeoutMiddleware': 350,
    'scrapy.downloadermiddlewares.defaultheaders.DefaultHeadersMiddleware': 400,
    'scrapy.downloadermiddlewares.useragent.UserAgentMiddleware': 500,
    'scrapy.downloadermiddlewares.retry.RetryMiddleware': 550,
    'scrapy.downloadermiddlewares.ajaxcrawl.AjaxCrawlMiddleware': 560,
    'scrapy.downloadermiddlewares.redirect.MetaRefreshMiddleware': 580,
    'scrapy.downloadermiddlewares.httpcompression.HttpCompressionMiddleware': 590,
    'scrapy.downloadermiddlewares.redirect.RedirectMiddleware': 600,
    'scrapy.downloadermiddlewares.cookies.CookiesMiddleware': 700,
    'scrapy.downloadermiddlewares.httpproxy.HttpProxyMiddleware': 750,
    'scrapy.downloadermiddlewares.stats.DownloaderStats': 850,
    'scrapy.downloadermiddlewares.httpcache.HttpCacheMiddleware': 900,

A dict containing the downloader middlewares enabled by default in Scrapy. Low orders are closer to the engine, high orders are closer to the downloader. You should never modify this setting in your project, modify DOWNLOADER_MIDDLEWARES instead. For more info see Activating a downloader middleware.


Default: True

Whether to enable downloader stats collection.


Default: 0

The amount of time (in secs) that the downloader should wait before downloading consecutive pages from the same website. This can be used to throttle the crawling speed to avoid hitting servers too hard. Decimal numbers are supported. Example:

DOWNLOAD_DELAY = 0.25    # 250 ms of delay

This setting is also affected by the RANDOMIZE_DOWNLOAD_DELAY setting (which is enabled by default). By default, Scrapy doesn’t wait a fixed amount of time between requests, but uses a random interval between 0.5 * DOWNLOAD_DELAY and 1.5 * DOWNLOAD_DELAY.

When CONCURRENT_REQUESTS_PER_IP is non-zero, delays are enforced per ip address instead of per domain.

You can also change this setting per spider by setting download_delay spider attribute.


Default: {}

A dict containing the request downloader handlers enabled in your project. See DOWNLOAD_HANDLERS_BASE for example format.



    'file': 'scrapy.core.downloader.handlers.file.FileDownloadHandler',
    'http': 'scrapy.core.downloader.handlers.http.HTTPDownloadHandler',
    'https': 'scrapy.core.downloader.handlers.http.HTTPDownloadHandler',
    's3': 'scrapy.core.downloader.handlers.s3.S3DownloadHandler',
    'ftp': 'scrapy.core.downloader.handlers.ftp.FTPDownloadHandler',

A dict containing the request download handlers enabled by default in Scrapy. You should never modify this setting in your project, modify DOWNLOAD_HANDLERS instead.

You can disable any of these download handlers by assigning None to their URI scheme in DOWNLOAD_HANDLERS. E.g., to disable the built-in FTP handler (without replacement), place this in your

    'ftp': None,

Default: 180

The amount of time (in secs) that the downloader will wait before timing out.


This timeout can be set per spider using download_timeout spider attribute and per-request using download_timeout Request.meta key.


Default: 1073741824 (1024MB)

The maximum response size (in bytes) that downloader will download.

If you want to disable it set to 0.


This size can be set per spider using download_maxsize spider attribute and per-request using download_maxsize Request.meta key.

This feature needs Twisted >= 11.1.


Default: 33554432 (32MB)

The response size (in bytes) that downloader will start to warn.

If you want to disable it set to 0.


This size can be set per spider using download_warnsize spider attribute and per-request using download_warnsize Request.meta key.

This feature needs Twisted >= 11.1.


Default: True

Whether or not to fail on broken responses, that is, declared Content-Length does not match content sent by the server or chunked response was not properly finish. If True, these responses raise a ResponseFailed([_DataLoss]) error. If False, these responses are passed through and the flag dataloss is added to the response, i.e.: 'dataloss' in response.flags is True.

Optionally, this can be set per-request basis by using the download_fail_on_dataloss Request.meta key to False.


A broken response, or data loss error, may happen under several circumstances, from server misconfiguration to network errors to data corruption. It is up to the user to decide if it makes sense to process broken responses considering they may contain partial or incomplete content. If RETRY_ENABLED is True and this setting is set to True, the ResponseFailed([_DataLoss]) failure will be retried as usual.


Default: 'scrapy.dupefilters.RFPDupeFilter'

The class used to detect and filter duplicate requests.

The default (RFPDupeFilter) filters based on request fingerprint using the scrapy.utils.request.request_fingerprint function. In order to change the way duplicates are checked you could subclass RFPDupeFilter and override its request_fingerprint method. This method should accept scrapy Request object and return its fingerprint (a string).

You can disable filtering of duplicate requests by setting DUPEFILTER_CLASS to 'scrapy.dupefilters.BaseDupeFilter'. Be very careful about this however, because you can get into crawling loops. It’s usually a better idea to set the dont_filter parameter to True on the specific Request that should not be filtered.


Default: False

By default, RFPDupeFilter only logs the first duplicate request. Setting DUPEFILTER_DEBUG to True will make it log all duplicate requests.


Default: vi (on Unix systems) or the IDLE editor (on Windows)

The editor to use for editing spiders with the edit command. Additionally, if the EDITOR environment variable is set, the edit command will prefer it over the default setting.


Default:: {}

A dict containing the extensions enabled in your project, and their orders.



    'scrapy.extensions.corestats.CoreStats': 0,
    'scrapy.extensions.telnet.TelnetConsole': 0,
    'scrapy.extensions.memusage.MemoryUsage': 0,
    'scrapy.extensions.memdebug.MemoryDebugger': 0,
    'scrapy.extensions.closespider.CloseSpider': 0,
    'scrapy.extensions.feedexport.FeedExporter': 0,
    'scrapy.extensions.logstats.LogStats': 0,
    'scrapy.extensions.spiderstate.SpiderState': 0,
    'scrapy.extensions.throttle.AutoThrottle': 0,

A dict containing the extensions available by default in Scrapy, and their orders. This setting contains all stable built-in extensions. Keep in mind that some of them need to be enabled through a setting.

For more information See the extensions user guide and the list of available extensions.


The Feed Temp dir allows you to set a custom folder to save crawler temporary files before uploading with FTP feed storage and Amazon S3.


Default: True

Whether or not to use passive mode when initiating FTP transfers.


Default: "guest"

The password to use for FTP connections when there is no "ftp_password" in Request meta.


Paraphrasing RFC 1635, although it is common to use either the password “guest” or one’s e-mail address for anonymous FTP, some FTP servers explicitly ask for the user’s e-mail address and will not allow login with the “guest” password.


Default: "anonymous"

The username to use for FTP connections when there is no "ftp_user" in Request meta.


Default: {}

A dict containing the item pipelines to use, and their orders. Order values are arbitrary, but it is customary to define them in the 0-1000 range. Lower orders process before higher orders.


    'mybot.pipelines.validate.ValidateMyItem': 300,
    'mybot.pipelines.validate.StoreMyItem': 800,

Default: {}

A dict containing the pipelines enabled by default in Scrapy. You should never modify this setting in your project, modify ITEM_PIPELINES instead.


Default: True

Whether to enable logging.


Default: 'utf-8'

The encoding to use for logging.


Default: None

File name to use for logging output. If None, standard error will be used.


Default: '%(asctime)s [%(name)s] %(levelname)s: %(message)s'

String for formatting log messsages. Refer to the Python logging documentation for the whole list of available placeholders.


Default: '%Y-%m-%d %H:%M:%S'

String for formatting date/time, expansion of the %(asctime)s placeholder in LOG_FORMAT. Refer to the Python datetime documentation for the whole list of available directives.


Default: 'DEBUG'

Minimum level to log. Available levels are: CRITICAL, ERROR, WARNING, INFO, DEBUG. For more info see Logging.


Default: False

If True, all standard output (and error) of your process will be redirected to the log. For example if you print 'hello' it will appear in the Scrapy log.


Default: False

If True, the logs will just contain the root path. If it is set to False then it displays the component responsible for the log output


Default: False

Whether to enable memory debugging.


Default: []

When memory debugging is enabled a memory report will be sent to the specified addresses if this setting is not empty, otherwise the report will be written to the log.



Default: True

Scope: scrapy.extensions.memusage

Whether to enable the memory usage extension. This extension keeps track of a peak memory used by the process (it writes it to stats). It can also optionally shutdown the Scrapy process when it exceeds a memory limit (see MEMUSAGE_LIMIT_MB), and notify by email when that happened (see MEMUSAGE_NOTIFY_MAIL).

See Memory usage extension.


Default: 0

Scope: scrapy.extensions.memusage

The maximum amount of memory to allow (in megabytes) before shutting down Scrapy (if MEMUSAGE_ENABLED is True). If zero, no check will be performed.

See Memory usage extension.


New in version 1.1.

Default: 60.0

Scope: scrapy.extensions.memusage

The Memory usage extension checks the current memory usage, versus the limits set by MEMUSAGE_LIMIT_MB and MEMUSAGE_WARNING_MB, at fixed time intervals.

This sets the length of these intervals, in seconds.

See Memory usage extension.


Default: False

Scope: scrapy.extensions.memusage

A list of emails to notify if the memory limit has been reached.



See Memory usage extension.


Default: 0

Scope: scrapy.extensions.memusage

The maximum amount of memory to allow (in megabytes) before sending a warning email notifying about it. If zero, no warning will be produced.


Default: ''

Module where to create new spiders using the genspider command.


NEWSPIDER_MODULE = 'mybot.spiders_dev'

Default: True

If enabled, Scrapy will wait a random amount of time (between 0.5 * DOWNLOAD_DELAY and 1.5 * DOWNLOAD_DELAY) while fetching requests from the same website.

This randomization decreases the chance of the crawler being detected (and subsequently blocked) by sites which analyze requests looking for statistically significant similarities in the time between their requests.

The randomization policy is the same used by wget --random-wait option.

If DOWNLOAD_DELAY is zero (default) this option has no effect.


Default: 10

The maximum limit for Twisted Reactor thread pool size. This is common multi-purpose thread pool used by various Scrapy components. Threaded DNS Resolver, BlockingFeedStorage, S3FilesStore just to name a few. Increase this value if you’re experiencing problems with insufficient blocking IO.


Default: 20

Defines the maximum times a request can be redirected. After this maximum the request’s response is returned as is. We used Firefox default value for the same task.


Default: +2

Scope: scrapy.downloadermiddlewares.redirect.RedirectMiddleware

Adjust redirect request priority relative to original request:

  • a positive priority adjust (default) means higher priority.
  • a negative priority adjust means lower priority.

Default: -1

Scope: scrapy.downloadermiddlewares.retry.RetryMiddleware

Adjust retry request priority relative to original request:

  • a positive priority adjust means higher priority.
  • a negative priority adjust (default) means lower priority.

Default: False

Scope: scrapy.downloadermiddlewares.robotstxt

If enabled, Scrapy will respect robots.txt policies. For more information see RobotsTxtMiddleware.


While the default value is False for historical reasons, this option is enabled by default in file generated by scrapy startproject command.


Default: 'scrapy.core.scheduler.Scheduler'

The scheduler to use for crawling.


Default: False

Setting to True will log debug information about the requests scheduler. This currently logs (only once) if the requests cannot be serialized to disk. Stats counter (scheduler/unserializable) tracks the number of times this happens.

Example entry in logs:

1956-01-31 00:00:00+0800 [scrapy.core.scheduler] ERROR: Unable to serialize request:
<GET> - reason: cannot serialize <Request at 0x9a7c7ec>
(type Request)> - no more unserializable requests will be logged
(see 'scheduler/unserializable' stats counter)

Default: 'scrapy.squeues.PickleLifoDiskQueue'

Type of disk queue that will be used by scheduler. Other available types are scrapy.squeues.PickleFifoDiskQueue, scrapy.squeues.MarshalFifoDiskQueue, scrapy.squeues.MarshalLifoDiskQueue.


Default: 'scrapy.squeues.LifoMemoryQueue'

Type of in-memory queue used by scheduler. Other available type is: scrapy.squeues.FifoMemoryQueue.


Default: 'queuelib.PriorityQueue'

Type of priority queue used by scheduler.


Default:: {}

A dict containing the spider contracts enabled in your project, used for testing spiders. For more info see Spiders Contracts.



    'scrapy.contracts.default.UrlContract' : 1,
    'scrapy.contracts.default.ReturnsContract': 2,
    'scrapy.contracts.default.ScrapesContract': 3,

A dict containing the scrapy contracts enabled by default in Scrapy. You should never modify this setting in your project, modify SPIDER_CONTRACTS instead. For more info see Spiders Contracts.

You can disable any of these contracts by assigning None to their class path in SPIDER_CONTRACTS. E.g., to disable the built-in ScrapesContract, place this in your

    'scrapy.contracts.default.ScrapesContract': None,

Default: 'scrapy.spiderloader.SpiderLoader'

The class that will be used for loading spiders, which must implement the SpiderLoader API.


New in version 1.3.3.

Default: False

By default, when scrapy tries to import spider classes from SPIDER_MODULES, it will fail loudly if there is any ImportError exception. But you can choose to silence this exception and turn it into a simple warning by setting SPIDER_LOADER_WARN_ONLY = True.


Some scrapy commands run with this setting to True already (i.e. they will only issue a warning and will not fail) since they do not actually need to load spider classes to work: scrapy runspider, scrapy settings, scrapy startproject, scrapy version.


Default:: {}

A dict containing the spider middlewares enabled in your project, and their orders. For more info see Activating a spider middleware.



    'scrapy.spidermiddlewares.httperror.HttpErrorMiddleware': 50,
    'scrapy.spidermiddlewares.offsite.OffsiteMiddleware': 500,
    'scrapy.spidermiddlewares.referer.RefererMiddleware': 700,
    'scrapy.spidermiddlewares.urllength.UrlLengthMiddleware': 800,
    'scrapy.spidermiddlewares.depth.DepthMiddleware': 900,

A dict containing the spider middlewares enabled by default in Scrapy, and their orders. Low orders are closer to the engine, high orders are closer to the spider. For more info see Activating a spider middleware.


Default: []

A list of modules where Scrapy will look for spiders.


SPIDER_MODULES = ['mybot.spiders_prod', 'mybot.spiders_dev']

Default: 'scrapy.statscollectors.MemoryStatsCollector'

The class to use for collecting stats, who must implement the Stats Collector API.


Default: True

Dump the Scrapy stats (to the Scrapy log) once the spider finishes.

For more info see: Stats Collection.


Default: [] (empty list)

Send Scrapy stats after spiders finish scraping. See StatsMailer for more info.


Default: True

A boolean which specifies if the telnet console will be enabled (provided its extension is also enabled).


Default: [6023, 6073]

The port range to use for the telnet console. If set to None or 0, a dynamically assigned port is used. For more info see Telnet Console.


Default: templates dir inside scrapy module

The directory where to look for templates when creating new projects with startproject command and new spiders with genspider command.

The project name must not conflict with the name of custom files or directories in the project subdirectory.


Default: 2083

Scope: spidermiddlewares.urllength

The maximum URL length to allow for crawled URLs. For more information about the default value for this setting see:


Default: "Scrapy/VERSION (+"

The default User-Agent to use when crawling, unless overridden.

Settings documented elsewhere:

The following settings are documented elsewhere, please check each specific case to see how to enable and use them.


Built-in Exceptions reference

Here’s a list of all exceptions included in Scrapy and their usage.

exception scrapy.exceptions.DropItem

The exception that must be raised by item pipeline stages to stop processing an Item. For more information see Item Pipeline.

exception scrapy.exceptions.CloseSpider(reason='cancelled')

This exception can be raised from a spider callback to request the spider to be closed/stopped. Supported arguments:

Parameters:reason (str) – the reason for closing

For example:

def parse_page(self, response):
    if 'Bandwidth exceeded' in response.body:
        raise CloseSpider('bandwidth_exceeded')
exception scrapy.exceptions.DontCloseSpider

This exception can be raised in a spider_idle signal handler to prevent the spider from being closed.

exception scrapy.exceptions.IgnoreRequest

This exception can be raised by the Scheduler or any downloader middleware to indicate that the request should be ignored.

exception scrapy.exceptions.NotConfigured

This exception can be raised by some components to indicate that they will remain disabled. Those components include:

  • Extensions
  • Item pipelines
  • Downloader middlewares
  • Spider middlewares

The exception must be raised in the component’s __init__ method.

exception scrapy.exceptions.NotSupported

This exception is raised to indicate an unsupported feature.

Command line tool
Learn about the command-line tool used to manage your Scrapy project.
Write the rules to crawl your websites.
Extract the data from web pages using XPath.
Scrapy shell
Test your extraction code in an interactive environment.
Define the data you want to scrape.
Item Loaders
Populate your items with the extracted data.
Item Pipeline
Post-process and store your scraped data.
Feed exports
Output your scraped data using different formats and storages.
Requests and Responses
Understand the classes used to represent HTTP requests and responses.
Link Extractors
Convenient classes to extract links to follow from pages.
Learn how to configure Scrapy and see all available settings.
See all available exceptions and their meaning.

Built-in services



scrapy.log has been deprecated alongside its functions in favor of explicit calls to the Python standard logging. Keep reading to learn more about the new logging system.

Scrapy uses Python’s builtin logging system for event logging. We’ll provide some simple examples to get you started, but for more advanced use-cases it’s strongly suggested to read thoroughly its documentation.

Logging works out of the box, and can be configured to some extent with the Scrapy settings listed in Logging settings.

Scrapy calls scrapy.utils.log.configure_logging() to set some reasonable defaults and handle those settings in Logging settings when running commands, so it’s recommended to manually call it if you’re running Scrapy from scripts as described in Run Scrapy from a script.

Log levels

Python’s builtin logging defines 5 different levels to indicate the severity of a given log message. Here are the standard ones, listed in decreasing order:

  1. logging.CRITICAL - for critical errors (highest severity)
  2. logging.ERROR - for regular errors
  3. logging.WARNING - for warning messages
  4. logging.INFO - for informational messages
  5. logging.DEBUG - for debugging messages (lowest severity)

How to log messages

Here’s a quick example of how to log a message using the logging.WARNING level:

import logging
logging.warning("This is a warning")

There are shortcuts for issuing log messages on any of the standard 5 levels, and there’s also a general logging.log method which takes a given level as argument. If needed, the last example could be rewritten as:

import logging
logging.log(logging.WARNING, "This is a warning")

On top of that, you can create different “loggers” to encapsulate messages. (For example, a common practice is to create different loggers for every module). These loggers can be configured independently, and they allow hierarchical constructions.

The previous examples use the root logger behind the scenes, which is a top level logger where all messages are propagated to (unless otherwise specified). Using logging helpers is merely a shortcut for getting the root logger explicitly, so this is also an equivalent of the last snippets:

import logging
logger =