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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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Katharine Jarmul Katharine Jarmul
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Katharine Jarmul
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Comparing performance

To help evaluate the trade-offs between the three scraping approaches described in the section, Three approaches to scrape a web page, it would be helpful to compare their relative efficiency. Typically, a scraper would extract multiple fields from a web page. So, for a more realistic comparison, we will implement extended versions of each scraper which extract all the available data from a country's web page. To get started, we need to return to our browser to check the format of the other country features, as shown here:

By using our browser's inspect capabilities, we can see each table row has an ID starting with places_ and ending with __row. The country data is contained within these rows in the same format as the area example. Here are implementations that use this information to extract all of the available country data:

FIELDS = ('area', 'population',...
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