Scraping data from websites is one of those things that sounds really cool and high-tech, but it's pretty simple and fun. In case you're too lazy to read the docs for Beautiful Soup and Requests, here's a quick and intro to webscraping in Python. The most basic functionality is demo'd below:

import requests
from bs4 import BeautifulSoup
url = "http://pitchfork.com/"
page = requests.get(url).text
soup = BeautifulSoup(page)

The soup object is basically a parsed representation of the page. That's right, Beautiful Soup does it for you. Once you've made your soup, it's easy to pull out who lists of objects with the find and findall methods.

h1 = find_all('h1')
link = find_all('a', href=True)

Now one caveat is that if you want if a particular element, you'll have to do a little nit-picking to get that out.

anchor_text = soup.find_all(class_='alt')[0].find_all('a')[1].text

Notice that you can adjust which element you're pointing at by ajusting the [i] that you're calling. This the part that can be both really frustrating if you're bad and really cool if you're less bad.

The fun part about webscraping in Python is that you have access to a lot of different tools after you've got the data: you can load it into a SQLite db using the sqlite module or into a pandas dataframe so you can analyze them. If you want to see an example of how webscraping can be used in a full scale project (along with some linear modeling tricks in Python) check out my recent project on GitHub.