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Web Scraping Best Practises

Web Scraping Best Practises

Python is a fantastic language for writing web scrapers. There is a large ecosystem of useful projects and a great developer community. However, it can be confusing once you go beyond the simpler scrapers typically covered in tutorials.

In this talk, from EuroPython 2015, we explore some common real-world scraping tasks. You will learn best practises and get a deeper understanding of what tools and techniques can be used and how to deal with the most challenging of web scraping projects.

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Shane Evans

July 21, 2015
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Transcript

  1. Web Scraping Best Practises Shane Evans @shaneaevans

  2. About Shane • 12y python, 8y scraping • Scrapy, Portia,

    Frontera.. • Co-founded Scrapinghub
  3. Why Scrape? Sources of data -> Internet vast amount of

    data APIs - Availability, Limited Data, Throttling, Privacy The web is broken - microdata, microformats, RDFa Endless use cases: monitor prices, leads generation, e- commerce, research, specialized news...
  4. Web Scraping Traffic

  5. Badly Written Bots Bots can: • use excessive website resources

    • be unreliable • be hard to maintain
  6. What is Web Scraping A technique of extracting information from

    websites includes: • downloading web pages (which may involve crawling - extracting and following links) • Scraping - extracting data from downloaded pages
  7. Web Scraping Example Our jobs get scraped and posted to

    jobs websites the day they are published! How would you build that web scraper? Scrapinghub gets scraped all the time
  8. Downloading with Requests For simple cases, use requests url =

    'https://ep2015.europython.eu/en/speakers/' r = requests.get(url) t.text Nice API, clean code!
  9. Crawling with Requests def __init__(self): self.session = requests.Session() def make_request(self,

    url): try: return self.session.get(url) except (requests.exceptions.HTTPError, requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as e: # TODO: some retry logic with logging and waits pass def start_crawl(self): response = self.make_request(self.base_url + '/en/speakers/') # TODO: extract links to speakers, call make_requests for each, then extract
  10. Crawling with Scrapy class EPSpeakerSpider(CrawlSpider): name = 'epspeakers_crawlspider' start_urls =

    ['https://ep2015.europython.eu/en/speakers/'] rules = [ Rule(LinkExtractor(allow=('/conference/',)), callback='parse_speakerdetails') ] def parse_speakerdetails(self, response): ... Scrapy is your friend
  11. Crawling Multiple Websites • Separate spiders for each different website

    (or crawl logic) • Common logic in appropriate places (middlewares, item loaders, etc.) • lots more: tool support, common patterns, etc. See demo at https://github.com/scrapinghub/pycon-speakers Scrapy encourages best practices for scraping multiple websites:
  12. Crawling - following links Crawling tips: • find good sources

    of links e.g. sitemaps • consider the crawl order - depth first, breadth first, priority • canonicalize and remove duplicates • beware of spider traps! - always add limits
  13. Crawling at Scale Lots of data: visited and discovered URLs

    Batch vs. Incremental crawling Different strategies for deciding what to crawl: - discover new content - revisit pages that are likely to have changed - prioritize relevant content Maintain politeness!
  14. Frontera • open source crawl frontier library, written in python

    • python API, or integrated with Scrapy • multiple crawl ordering algorithms (priority, HITS, pagerank, etc.) • configurable back ends: ◦ integrated or distributed ◦ different crawl orderings ◦ sqlite, hbase, etc. • works at scale
  15. Downloading Summary • requests is a great library for HTTP

    • scrapify early, especially if you do any crawling • frontera for advanced URL orderings or larger scale crawling
  16. Extraction - Standard Library We have a great standard library:

    • string handling: slice, split, strip, lower, etc. • regular expressions Usually used with other techniques
  17. Extraction - HTML Parsers HTML Parsers are the go-to tools

    for web scrapers! Useful when data can be extracted via the structure of the HTML document
  18. Extraction - HTML Parsers <html> <head></head> <body> <div>TEXT-1</div> <div> TEXT-2

    <b>TEXT-3</b> </div> <b>TEXT-4</b> </body> </html>
  19. Extraction - HTML Parsers HTML

  20. Extraction - XPath XPath //b

  21. Extraction - XPath XPath //div/b

  22. Extraction - XPath XPath //div[2]/text()

  23. Extraction - XPath XPath //div[2]//text()

  24. Scrapy Selectors name = response.xpath('//section[@class="profile-name"]//h1/text()').extract_first() avatar = response.urljoin(response.xpath('//img[@class="avatar"]/@src').extract_first() for talk

    in response.xpath('//div[@class="speaker-talks well"]//li'): talk_title = talk.xpath('.//text()').extract_first() xpath using Scrapy selectors
  25. BeautifulSoup name = soup.find('section', attrs={'class': 'profile-name'}).h1.text item['avatar'] = self.base_url +

    soup.find('img', attrs={'class': 'avatar'})['src'] for talk in soup.find('div', attrs={'class': 'speaker-talks well'}).dl.dd.ul.li: title = talk.text BeautifulSoup uses python objects to interact with the parse tree
  26. Someone might have solved it! Don’t reinvent the wheel! Lots

    of small tools that can make your life easier, for example: • Scrapy loginform fills in login forms • dateparser - parse text dates • webpager helps with pagination
  27. Visual Data Extraction Portia is a Visual Scraper written in

    Python! • train by annotating web pages • run with Scrapy
  28. Scaling Extraction At scale, use methods that do not require

    additional work per website • boilerplate removal - python- goose, readability, justext.. • analyze text with nltk • scikit-learn and scikit-image for classification, feature extraction • webstruct - NER with HTML
  29. Named Entity Recognition • finds and classifies elements in text

    into predefined categories • examples include person names or job titles, organizations, locations, expressions of times, quantities, monetary values, percentages, etc. For English it is often solved using machine learning
  30. Web Page Annotation Web pages need to be annotated manually

    Useful tools include: • https://github.com/xtannier/WebAnnotator • https://gate.ac.uk/ • http://brat.nlplab.org/
  31. Labeling Name entity > one or more tokens This data

    format is not convenient for ML algorithms
  32. Encoding IOB encoding • Tokens 'outside' named entities - tag

    O • The first token in entity - tag B-ENTITY • Other tokens of an entity - tag I-ENTITY
  33. Classification Task The problem is reduced to a "standard" ML

    classification task • Input data - information about tokens (==features) • Output data - named entity label, encoded as IOB • Use a classifier which takes the order of predicted labels into account (Conditional Random Fields is a common choice)
  34. Feature Examples • token == "Cafe"? • is the first

    letter uppercase? • is token a name of a month? • are the two previous tokens "© 2014"? • is the token inside a <title> HTML element?
  35. Putting it together One way to do it: • WebAnnotator

    to annotate pages manually • WebStruct to load training data (annotated pages) and encode named entity labels to IOB • write Python functions to extract features (and/or use some of the WebStruct feature extraction functions) • train a CRF model using python-crfsuite • WebStruct to combine all the pieces
  36. Data Extraction Summary

  37. Saatchi Global Gallery Guide • Scrape 11k+ gallery websites •

    Extract artworks, artist and exhibitions
  38. Saatchi Global Gallery Guide Crawling: • Use Scrapy, running a

    batch of sites per process. Many processes at once. • Deploy on Scrapy Cloud • Rank links to follow, prioritizing likely sources of content • scikit-learn for selecting links to crawl • webpager to follow pagination • Limit crawl depth and requests per website
  39. Saatchi Global Gallery Guide Extraction: • webstruct for all structured

    data extraction • scikit-learn for feature extraction • scikit-image - face recognition to distinguish between artists and artworks • fuzzywuzzy string matching • goose to clean input html • Store item hashes and only export updates
  40. Saatchi Global Gallery Guide r = requests.get('http://oh-wow.com/artists/charlie-billingham/') response = scrapy.TextResponse(r.url,

    body=r.text, encoding='utf-8', headers={'content-type': 'text/html'}) ext = ArtworkExtractor() print ext.extract_artworks(response) [{'MEDIUM': u'Oil and acrylic on linen', 'PHOTO':'http://oh-wow.com/wp-content/uploads/2014/0...', 'SIZE': u'39.5 x 31.5 inches 100.3 x 80 cm', 'TITLE': u'Unforced Error', 'YEAR': u'2015'}] CHARLIE BILLINGHAM Unforced Error, 2015 Artwork extraction example
  41. Saatchi Global Gallery Guide evaluation: • measure accuracy (precision and

    recall) • avoid false positives • test everything, improve iteratively
  42. Web Scraping Challenges Difficulty typically depends on the size of

    data, complexity of extracted items and accuracy requirements
  43. Web Scraping Challenges But getting clean data from the web

    is a dirty business! Some things will kill your scraping..
  44. Irregular Structure HTML parsers, our go-to tool, require sane and

    consistent HTML Structure In practise, some websites will: • use many different templates • run multivariate testing • have very, very broken HTML
  45. JavaScript Many sites require JavaScript, or browser rendering, to be

    scraped. • Splash is a scriptable browser available via an API. Works well with Scrapy. • Automate web browser interaction with Selenium • Most JS-heavy sites call APIs. You can do that too! use browser tools to inspect • js2xml can make JS easier to parse, sometimes pull data with regexp • Maybe the mobile site is better?
  46. Proxies • Sometimes need to crawl from a specific location

    • Many hosting centres (e.g. EC2) are frequently entirely banned • Privacy can be important • Multiple proxies for sustained reliable crawling e.g. Tor, illuminati, open proxies, private providers and Crawlera
  47. Ethics • is your web scraping causing harm? • crawl

    at a reasonable rate, especially on smaller websites • identify your bot via a user agent • respect robots.txt on broad crawls
  48. Q & A Ask me anything..

  49. THANK YOU! Shane Evans @shaneaevans Visit our booth Talk to

    us at the recruiting session Stick around for the “Dive into Scrapy” talk