Scrapy is an open source and Python-based web crawling framework. It provides tools and components for writing web spiders to extract structured data from websites, including built-in selectors, item pipelines, link extractors, and request/response handling. Spiders define rules for crawling sites by following links and extracting items using selectors, which are then passed through the framework's asynchronous data flow and stored or processed using items pipelines.

1. Scrapy
Patrick O’Brien | @obdit
DataPhilly | 20131118 | Monetate

2. Steps of data science
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Obtain
Scrub
Explore
Model
iNterpret

3. Steps of data science
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Obtain
Scrub
Explore
Model
iNterpret
Scrapy

4. About Scrapy
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Framework for collecting data
Open source - 100% python
Simple and well documented
OSX, Linux, Windows, BSD

5. Some of the features
● Built-in selectors
● Generating feed output
○ Format: json, csv, xml
○ Storage: local, FTP, S3, stdout
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Encoding and autodetection
Stats collection
Control via a web service
Handle cookies, auth, robots.txt, user-agent

6. Scrapy Architecture

7. Data flow
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Engine opens, locates Spider, schedule first url as a Request
Scheduler sends url to the Engine, which sends it to Downloader
Downloader sends completed page as a Response through the
middleware to the engine
Engine sends Response to the Spider through middleware
Spiders sends Items and new Requests to the Engine
Engine sends Items to the Item Pipeline and Requests to the Scheduler
GOTO 2

8. Parts of Scrapy
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Items
Spider
Link Extractors
Selectors
Request
Responses

9. Items
● Main container of structured information
● dict-like objects
from scrapy.item import Item, Field
class Product(Item):
name = Field()
price = Field()
stock = Field()
last_updated = Field(serializer=str)

10. Items
>>> product = Product(name='Desktop PC', price=1000)
>>> print product
Product(name='Desktop PC', price=1000)
>>> product['name']
Desktop PC
>>> product.get('name')
Desktop PC
>>> product.keys()
['price', 'name']
>>> product.items()
[('price', 1000), ('name', 'Desktop PC')]

11. Spiders
● Define how to move around a site
○ which links to follow
○ how to extract data
● Cycle
○ Initial request and callback
○ Store parsed content
○ Subsequent requests and callbacks

12. Generic Spiders
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BaseSpider
CrawlSpider
XMLFeedSpider
CSVFeedSpider
SitemapSpider

13. BaseSpider
● Every other spider inherits from BaseSpider
● Two jobs
○ Request `start_urls`
○ Callback `parse` on resulting response

14. BaseSpider
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class MySpider(BaseSpider):
name = 'example.com'
allowed_domains = ['example.com']
start_urls = [
● Send Requests example.
com/[1:3].html
'http://www.example.com/1.html',
'http://www.example.com/2.html',
'http://www.example.com/3.html',
]
● Yield title Item
def parse(self, response):
sel = Selector(response)
for h3 in sel.xpath('//h3').extract():
yield MyItem(title=h3)
for url in sel.xpath('//a/@href').extract():
yield Request(url, callback=self.parse)
● Yield new Request

15. CrawlSpider
● Provide a set of rules on what links to follow
○ `link_extractor`
○ `call_back`
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(SgmlLinkExtractor(allow=('category.php', ), deny=('subsection.php', ))),
# Extract links matching 'item.php' and parse them with the spider's method parse_item
Rule(SgmlLinkExtractor(allow=('item.php', )), callback='parse_item'),
)

16. Link Extractors
● Extract links from Response objects
● Parameters include:
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allow | deny
allow_domains | deny_domains
deny_extensions
restrict_xpaths
tags
attrs

17. Selectors
● Mechanisms for extracting data from HTML
● Built over the lxml library
● Two methods
○ XPath: sel.xpath('//a[contains(@href,
"image")]/@href' ).
extract()
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CSS: sel.css('a[href*=image]::attr(href)' ).extract()
● Response object is called into Selector
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sel = Selector(response)

18. Request
● Generated in Spider, sent to Downloader
● Represent an HTTP request
● FormRequest subclass performs HTTP
POST
○ useful to simulate user login

19. Response
● Comes from Downloader and sent to Spider
● Represents HTTP response
● Subclasses
○ TextResponse
○ HTMLResponse
○ XmlResponse

20. Advanced Scrapy
● Scrapyd
○ application to deploy and run Scrapy spiders
○ deploy projects and control with JSON API
● Signals
○ notify when events occur
○ hook into Signals API for advance tuning
● Extensions
○ Custom functionality loaded at Scrapy startup

21. More information
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http://doc.scrapy.org/
https://twitter.com/ScrapyProject
https://github.com/scrapy
http://scrapyd.readthedocs.org

22. Demo