added a basic webscraping tutorial

requirements.txt

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+ipython
+requests
+yolk

tutorials/webscraping101/failed_banks_scrape.py

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+#!/usr/bin/env python
+"""
+This is the first example scrape in our series.
+
+In this scrape, we'll demonstrate some Python basics
+using the FDIC's Failed Banks List.
+
+USAGE:
+You can run this scrape by going to command line, navigating to the
+directory containing this script, and typing the below command:
+
+    python failed_banks_scrape.py
+
+
+NOTE:
+The original FDIC data is located at the below URL:
+
+    http://www.fdic.gov/bank/individual/failed/banklist.html
+
+In order to be considerate to the FDIC's servers, we're scraping 
+a copy of the page stored on one of Amazon S3.
+
+
+
+"""
+# Import a built-in library for working with data on the Web
+# DOCS: http://docs.python.org/library/urllib.html
+import urllib
+
+# import a 3rd-party to help extract data from raw HTML
+# DOCS: http://www.crummy.com/software/BeautifulSoup/documentation.html  
+from BeautifulSoup import BeautifulSoup
+
+# URL of the page we're going to scrape (below is the real URL, but
+# we'll hit a dummy version to be kind to the FDIC)
+URL = 'https://s3.amazonaws.com/python-journos/FDIC_Failed_Bank_List.html'
+
+# Open a network connection using the "urlopen" method. 
+# This returns a network "object" 
+# http://docs.python.org/library/urllib.html#high-level-interface
+web_cnx = urllib.urlopen(URL)
+
+# Use the network object to download, or "read", the page's HTML
+html = web_cnx.read() 
+
+# Parse the HTML into a form that's easy to use
+soup = BeautifulSoup(html)
+
+# Use BeautifulSoup's API to extract your data
+# 1) Fetch the table by ID
+table  = soup.find(id='table') 
+
+# 2) Grab the table's rows
+rows = table.findAll('tr')
+
+# 3) Get header names from first row
+headers = rows[0].findAll('th')
+
+# Extract the column names and add them to a list
+columns = []
+for header in headers:
+    columns.append(header.text)
+
+# Use the tab character's 'join' method to concatenate
+# the column names into a single, tab-separated string.
+# Then print out the header column.
+print '\t'.join(columns)
+
+# 4) Process the data, skipping the initial header row
+for row in rows[1:]:
+
+    # Extract the data points from the table row and print them
+    data = row.findAll('td')
+    bank_name = data[0].text    
+    city = data[1].text
+    state = data[2].text
+    cert_num = data[3].text
+    ai =  data[4].text
+    closed_on = data[5].text 
+    updated = data[6].text
+    print "\t".join([bank_name, city, state, cert_num, ai, closed_on, updated])