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+HTML Scraping
+=============
+
+Web Scraping
+------------
+
+Web sites are written using HTML, which means that each web page is a
+structured document. Sometimes it would be great to obtain some data from 
+them and preserve the structure while we're at it. Web sites provide 
+don't always provide their data in comfortable formats such as ``.csv``. 
+
+This is where web scraping comes in. Web scraping is the practice of using a
+computer program to sift through a web page and gather the data that you need
+in a format most useful to you while at the same time preserving the structure
+of the data.
+
+lxml and Requests
+-----------------
+
+`lxml <http://lxml.de/>`_ is a pretty extensive library written for parsing
+XML and HTML documents really fast. It even handles messed up tags. We will 
+also be using the `Requests <http://docs.python-requests.org/en/latest/>`_ module instead of the already built-in urlib2 
+due to improvements in speed and readability. You can easily install both 
+using ``pip install lxml`` and ``pip install requests``.
+
+Lets start with the imports:
+
+.. code-block:: python
+
+    from lxml import html
+    import requests
+    
+Next we will use ``requests.get`` to retrieve the web page with our data 
+and parse it using the ``html`` module and save the results in ``tree``:
+
+.. code-block:: python
+
+    page = requests.get('http://econpy.pythonanywhere.com/ex/001.html')
+    tree = html.fromstring(page.text)
+
+``tree`` now contains the whole HTML file in a nice tree structure which
+we can go over two different ways: XPath and CSSSelect. In this example, I
+will focus on the former. 
+
+XPath is a way of locating information in structured documents such as 
+HTML or XML documents. A good introduction to XPath is on `W3Schools <http://www.w3schools.com/xpath/default.asp>`_ .
+
+There are also various tools for obtaining the XPath of elements such as
+FireBug for Firefox or if you're using Chrome you can right click an 
+element, choose 'Inspect element', highlight the code and then right 
+click again and choose 'Copy XPath'.
+
+After a quick analysis, we see that in our page the data is contained in 
+two elements - one is a div with title 'buyer-name' and the other is a 
+span with class 'item-price':
+
+::
+
+    <div title="buyer-name">Carson Busses</div>
+    <span class="item-price">$29.95</span>
+
+Knowing this we can create the correct XPath query and use the lxml
+``xpath`` function like this:
+
+.. code-block:: python
+
+    #This will create a list of buyers:
+    buyers = tree.xpath('//div[@title="buyer-name"]/text()')
+    #This will create a list of prices
+    prices = tree.xpath('//span[@class="item-price"]/text()')
+
+Lets see what we got exactly:
+
+.. code-block:: python
+
+    print 'Buyers: ', buyers
+    print 'Prices: ', prices
+
+::
+
+    Buyers:  ['Carson Busses', 'Earl E. Byrd', 'Patty Cakes', 
+    'Derri Anne Connecticut', 'Moe Dess', 'Leda Doggslife', 'Dan Druff',
+    'Al Fresco', 'Ido Hoe', 'Howie Kisses', 'Len Lease', 'Phil Meup',
+    'Ira Pent', 'Ben D. Rules', 'Ave Sectomy', 'Gary Shattire',
+    'Bobbi Soks', 'Sheila Takya', 'Rose Tattoo', 'Moe Tell']
+    
+    Prices:  ['$29.95', '$8.37', '$15.26', '$19.25', '$19.25',
+    '$13.99', '$31.57', '$8.49', '$14.47', '$15.86', '$11.11',
+    '$15.98', '$16.27', '$7.50', '$50.85', '$14.26', '$5.68',
+    '$15.00', '$114.07', '$10.09']
+
+Congratulations! We have successfully scraped all the data we wanted from
+a web page using lxml and Requests. We have it stored in memory as two 
+lists. Now we can do all sorts of cool stuff with it: we can analyze it 
+using Python or we can save it a file and share it with the world.
+
+A cool idea to think about is modifying this script to iterate through 
+the rest of the pages of this example dataset or rewriting this 
+application to use threads for improved speed.