Web Scraping in Python
Learn to retrieve and parse information from the internet using the Python library scrapy.
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Course Description
The ability to build tools capable of retrieving and parsing information stored across the internet has been and continues to be valuable in many veins of data science. In this course, you will learn to navigate and parse html code, and build tools to crawl websites automatically. Although our scraping will be conducted using the versatile Python library scrapy, many of the techniques you learn in this course can be applied to other popular Python libraries as well, including BeautifulSoup and Selenium. Upon the completion of this course, you will have a strong mental model of html structure, will be able to build tools to parse html code and access desired information, and create a simple scrapy spiders to crawl the web at scale.
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Get your team access to the full DataCamp platform, including all the features.In the following Tracks
Python Developer
Go To Track- 1
Introduction to HTML
FreeLearn the structure of HTML. We begin by explaining why web scraping can be a valuable addition to your data science toolbox and then delving into some basics of HTML. We end the chapter by giving a brief introduction on XPath notation, which is used to navigate the elements within HTML code.
- 2
XPaths and Selectors
Leverage XPath syntax to explore scrapy selectors. Both of these concepts will move you towards being able to scrape an HTML document.
XPathology50 xpCounting Elements in the Wild50 xpBody Appendages100 xpChoose DataCamp!100 xpOff the Beaten XPath50 xpWhere it's @100 xpCheck your Class100 xpHyper(link) Active100 xpSecret Links100 xpSelector Objects50 xpXPath Chaining100 xpDivvy Up This Exercise100 xpThe Source of the Source50 xpCourse Class by Inspection50 xpRequesting a Selector100 xp - 3
CSS Locators, Chaining, and Responses
Learn CSS Locator syntax and begin playing with the idea of chaining together CSS Locators with XPath. We also introduce Response objects, which behave like Selectors but give us extra tools to mobilize our scraping efforts across multiple websites.
From XPath to CSS50 xpThe (X)Path to CSS Locators100 xpGet an "a" in this Course100 xpThe CSS Wildcard100 xpCSS Attributes and Text Selection50 xpYou've been `href`ed100 xpTop Level Text100 xpAll Level Text100 xpRespond Please!50 xpReveal By Response100 xpResponding with Selectors100 xpSelecting from a Selection100 xpSurvey50 xpTitular100 xpScraping with Children100 xp - 4
Spiders
Learn to create web crawlers with scrapy. These scrapy spiders will crawl the web through multiple pages, following links to scrape each of those pages automatically according to the procedures we've learned in the previous chapters.
Your First Spider50 xpInheriting the Spider100 xpHurl the URLs100 xpStart Requests50 xpSelf Referencing is Classy100 xpStarting with Start Requests100 xpParse and Crawl50 xpPen Names100 xpCrawler Time100 xpCapstone50 xpTime to Run100 xpDataCamp Descriptions100 xpCapstone Crawler100 xpThe Finale50 xp
Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.In the following Tracks
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DataCamp webpage HTMLcollaborators
prerequisites
Intermediate Python
Thomas LaetschData Scientist at New York University
Since January 2016, Thomas Laetsch has been a Moore-Sloan Post-Doctoral Associate in the Center for Data Science at NYU. In 2012, he received his PhD in mathematics from the University of California, San Diego, specializing in probability, differential geometry, and functional analysis. From 2012 through 2015, he was a Visiting Assistant Professor at the University of Connecticut, working on central tendency theorems for random walks in degenerate spaces.
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