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Web Scraping in Python

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4 hours
4,500 XP
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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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  1. 1

    Introduction to HTML


    Learn 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.

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    Web Scraping Overview
    50 xp
    Web-scraping is not nonsense!
    50 xp
    HyperText Markup Language
    50 xp
    HTML tree wordy navigation
    50 xp
    From Tree to HTML
    100 xp
    50 xp
    Keep it Classy
    100 xp
    Finding href
    50 xp
    Crash Course in XPath
    50 xp
    Where am I?
    100 xp
    It's Time to P
    100 xp
    A classy span
    100 xp
  2. 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.

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  3. 4


    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.

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In the following tracks

Python Developer


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David Campos
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Mari Nazary
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Shon Inouye


Intermediate Python
Thomas Laetsch HeadshotThomas Laetsch

Data 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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