Hi! I haven’t posted in a while: I got displaced by Sandy, distracted by job applications, and overrun by zombies. It happens. But back to business…

One of the biggest tasks facing data scientists — and one that distinguishes them from traditional business analysts — is fetching and cleaning data. Once upon a time (or so I’m told), data was kept in orderly, consolidated databases, there wasn’t so much that size was an issue, and analysts could access it in a relatively straightforward manner through, say, a structured query language (SQL). Then the Internet happened, and data got Big and messy, and the process of getting it for analysis turned into a chore. Unfortunately, point-and-click doesn’t scale.

So what’s a data-starved data scientist to do?!? Well, one way to fetch data is through web scraping. (I rely on Wikipedia links for details far too much, but it’s just so easy: web scraping.) Basically, it is the automated extraction of data from resources served over HTTP and encoded in HTML or JavaScript. It’s related to the web indexing that search engines like Google continuously perform in order to keep track of all the content on the web and help people find the information they’re searching for, but the focus of web scraping is usually to retrieve unstructured, online data and transform it into a more structured, local form. For this to work, you’ll probably have to parse the HTML and separate the information you want from all the markup (it is, after all, a HyperText Markup Language) that goes into making a web page look as it does in a browser.

Let’s consider a simple case: You want to know tomorrow’s local weather forecast, but you don’t feel like going to the website, typing in the search bar, and dealing with all the advertisements. So, you write a little program that sends an HTTP request to weather.com’s server, which responds with (among other things) the HTML content you asked for, then you parse that HTML to find the string of characters embedded in a deep hierarchy of tags corresponding to the temperature: 48.

This example is kind of ridiculous, I know –— just bookmark the damn site! –— but vastly more complicated web scraping tasks can be built up from this basic procedure: request HTML, parse HTML, extract data (repeat). Maybe you’d like to compile a list of abilities of all superheroes on Wikipedia, or get U.S. election results by district from this guy without paying lots of money for his already-structured and -cleaned Excel spreadsheets, or get the Metacritic scores of all horror films in the past ten years. Sure, given enough time and patience, you could probably do this manually, but it’s much much easier to automate through code.

Although you can do limited web scraping tasks directly from a command line (with the curl, grep, and awk commands, among others), it’s nicer to work in a full-fledged scripting language. Python, for example, is great for web scraping and HTML parsing! Happily, people have written libraries and even entire frameworks specifically for these purposes:

• Scrapy: Free web scraping and crawling framework. You pick a website, specify the kind of data you want to extract and the rules to follow in finding/extracting it, then let Scrapy do its thing. It has built-in support for reading and cleaning the scraped data, and much more.
• Requests: Free and user-friendly HTTP library. Easily add options to your web query, read and properly encode the web server’s response, deal with authentication, etc.
• BeautifulSoup: Free HTML parsing library. Provides methods for navigating, searching, and modifying the parse tree, which saves you a lot of time.