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Python Web Scraping

You're reading from   Python Web Scraping Hands-on data scraping and crawling using PyQT, Selnium, HTML and Python

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Product type Paperback
Published in May 2017
Publisher
ISBN-13 9781786462589
Length 220 pages
Edition 2nd Edition
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Author (1):
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Katharine Jarmul Katharine Jarmul
Author Profile Icon Katharine Jarmul
Katharine Jarmul
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Automated scraping with Scrapely

For scraping the annotated fields Portia uses a library called Scrapely (https://github.com/scrapy/scrapely), which is a useful open-source tool developed independently from Portia. Scrapely uses training data to build a model of what to scrape from a web page. The trained model can then be applied to scrape other web pages with the same structure.

You can install it using pip:

pip install scrapely

Here is an example to show how it works:

  
>>> from scrapely import Scraper
>>> s = Scraper()
>>> train_url = 'http://example.webscraping.com/view/Afghanistan-1'
>>> s.train(train_url, {'name': 'Afghanistan', 'population': '29,121,286'})
>>> test_url = 'http://example.webscraping.com/view/United-Kingdom-239'
>>> s.scrape(test_url)
[{u'name': [u'United Kingdom&apos...
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